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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