umdhmm-v1.02
所属分类:人工智能/神经网络/深度学习
开发工具:C/C++
文件大小:267KB
下载次数:72
上传日期:2010-04-06 04:43:41
上 传 者:
geliang2008
说明: Hiden Markov Model的C语言实现,目前比较好的一个实现。用于机器学习,模式识别,分类算法
(Hiden Markov Model of the C language, an implementation of the present better. For machine learning, pattern recognition, classification algorithm)
文件列表:
umdhmm-v1.02 (0, 2010-03-16)
umdhmm-v1.02\t2.1500.seq (3009, 1999-05-06)
umdhmm-v1.02\testvit (50940, 1999-05-06)
umdhmm-v1.02\COPYING (17976, 1999-05-06)
umdhmm-v1.02\genseq.o (11192, 1999-05-06)
umdhmm-v1.02\hmm.h (2539, 1999-05-06)
umdhmm-v1.02\t2.hmm (116, 1999-05-06)
umdhmm-v1.02\t3.hmm (112, 1999-05-06)
umdhmm-v1.02\test.hmm (126, 1999-05-06)
umdhmm-v1.02\test.seq (26, 1999-05-06)
umdhmm-v1.02\hmmrand.c (718, 1999-05-06)
umdhmm-v1.02\sequence.o (8192, 1999-05-06)
umdhmm-v1.02\testfor.c (1833, 1999-05-06)
umdhmm-v1.02\nrutil.o (11252, 1999-05-06)
umdhmm-v1.02\testfor (49360, 1999-05-06)
umdhmm-v1.02\t2.100.seq (213, 1999-05-06)
umdhmm-v1.02\esthmm.c (5365, 1999-05-06)
umdhmm-v1.02\genseq (47120, 1999-05-06)
umdhmm-v1.02\hmmutils.o (11196, 1999-05-06)
umdhmm-v1.02\nrutil.h (764, 1999-05-06)
umdhmm-v1.02\hmmrand.o (6352, 1999-05-06)
umdhmm-v1.02\TODO (730, 1999-05-06)
umdhmm-v1.02\testvit.o (7648, 1999-05-06)
umdhmm-v1.02\esthmm (71068, 1999-05-06)
umdhmm-v1.02\VERSION (5, 1999-05-06)
umdhmm-v1.02\sequence.c (2852, 1999-05-06)
umdhmm-v1.02\Makefile (1143, 1999-05-06)
umdhmm-v1.02\viterbi.o (8824, 1999-05-06)
umdhmm-v1.02\nrutil.c (3980, 1999-05-06)
umdhmm-v1.02\hmmtut.pdf (180218, 1999-05-06)
umdhmm-v1.02\backward.c (2012, 1999-05-06)
umdhmm-v1.02\testvit.c (2427, 1999-05-06)
umdhmm-v1.02\testfor.o (7152, 1999-05-06)
umdhmm-v1.02\genseq.c (2640, 1999-05-06)
umdhmm-v1.02\forward.c (2215, 1999-05-06)
umdhmm-v1.02\baum.o (10772, 1999-05-06)
umdhmm-v1.02\baum.c (3968, 1999-05-06)
umdhmm-v1.02\hmmutils.c (3954, 1999-05-06)
umdhmm-v1.02\hmmtut.ps (254653, 1999-05-06)
... ...
$Id: README,v 1.5 19***/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, 19***
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, 1***9.
- Rabiner, L. R., and B. H. Juang, "An Introduction to Hidden Markov Models,"
IEEE ASSP Magazine, vol. 3, no. 1, pp. 4-16, Jan. 1***6.
---------------------------------------------
Installation:
---------------------------------------------
--------------------
UNIX: Dec, Sun Solaris, Linux (redhat):
--------------------
Type "make all" at the unix prompt. It should
compile the package.
--------------------
Microsoft NT/95/***:
--------------------
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=
N=
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=
o1 o2 o3 . . . oT
---------------------------------------------
Sample sequence file:
---------------------------------------------
T= 10
1 1 1 1 2 1 2 2 2 2
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