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
in the jahmm/src
directory. Thus, simply calling javac
with all the
.java files held in the jahmm/src
directory as arguments compiles everything.
Jahmm requires Java 1.5.0
.
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
(e.g. 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.
Regression (JUnit
) tests have also been written ; see the jahm/test
directory.
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 filesREADME.md
: this file.CHANGES
: changelog.ORIGINAL-LICENSE
: license file.ORIGINAL-THANKS
: contributors.
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 [email protected] (for questions/comments)
The author of this repository is Willem Van Onsem [email protected]
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 jadetree
package.