JavaMI, A java port of MIToolbox

This toolbox is aimed at people working on discrete datasets for classification. All functions expect discrete inputs. It provides implementations of Shannon's Information Theory functions. The toolbox was developed to support our research into feature selection algorithms. This is a port from the C version of the MIToolbox available here.

Note: all functions are calculated in log base 2, so return units of "bits".

Contains functions for:

Calculating Entropy, H(X)
Calculating Conditional Entropy, H(X|Y)
Calculating Mutual Information, I(X;Y)
Calculating Conditional Mutual Information, I(X;Y|Z)
Generating a joint random variable

The Javadoc for the code is available starting here

All JavaMI code is licensed under the LGPL v3.

If you use this toolbox for academic research please cite as:
A. Pocock, JavaMI Toolbox, University of Manchester, 2012.
URL: http://www.cs.man.ac.uk/~gbrown/software/

Download links:

ZIP TAR

Update History

20/01/2012 - v1.00 - Initial Release