Jahmm is a Java library implementing the various, well-known algorithms
related to Hidden Makov Models (HMMs for short).
The source code of the library is available; it is licensed under GPL (see the resource/COPYING file).
This library is short and simple. It's been written for clarity. It is particularly well suited for research and academic use.
The website associated to this library is: [http://jahmm.googlecode.com/] Most information related to this software can be found there.
This repository is a fork of the original jahmm library that can be found here: [http://jahmm.googlecode.com/]
To compile the library, you simply need to compile all the files held
jahmm/src directory. Thus, simply calling
javac with all the
.java files held in the
jahmm/src directory as arguments compiles everything.
To use it, simply launch:
javac -classpath /path/to/jahmm-<version>.jar Myprogram.java
to compile your program, and:
java -cp /path/to/jahmm-<version>.jar myMainClass
java -cp /home/smith/java_class/jahmm-0.6.2.jar test/Testing)
...to run it.
You can also put the
.jar file in your classpath.
JUnit) tests have also been written ; see the
pom.xml: the 'maven' project file.
build.xml: the 'ant' build file.
src/: all the .java files.
src/.../distributions: Pseudo random distributions.
src/.../jahmm: The jahmm library itself. This directory holds one directory per java package; see the jahmm website for more information about each of them.
test/: Regression tests.
examples: various example files
README.md: this file.
ORIGINAL-LICENSE: license file.
The program uses a java library called
jutils that can be found here: https://github.com/KommuSoft/jutil
Jahmm's original author is Jean-Marc Francois.
Feel free to send comments and questions related to this library at:
- http://code.google.com/p/jahmm/issues/list (if you have an issue with the library)
- http://groups.google.com/group/jahmm-discuss or firstname.lastname@example.org (for questions/comments)
The author of this repository is Willem Van Onsem Willem.VanOnsem@cs.kuleuven.be
this version aims to improve speed and enables the use of more sophisticated hidden markov
models like the Input-Output Hidden Markov Model (IOHMM). Furthermore decision trees are implemented in the