v2.1.1 for C/C++ and MATLAB/Octave
MIToolbox contains a set of functions to calculate information theoretic quantities from data, such as the entropy and mutual information. The toolbox contains implementations of the most popular Shannon entropies, and also the lesser known Renyi entropy. The toolbox also provides implementations of the weighted entropy and weighted mutual information from "Information Theory with Application", S. Guiasu (1977). The toolbox only supports discrete distributions, as opposed to continuous. All real-valued numbers will be processed by x = floor(x).
These functions are targeted for use with feature selection algorithms rather than communication channels and so expect all the data to be available before execution and sample their own probability distributions from the data.
- Conditional Entropy
- Mutual Information
- Conditional Mutual Information
- generating a joint variable
- generating a probability distribution from a discrete random variable
- Renyi's Entropy
- Renyi's Mutual Information
- Weighted Entropy
- Weighted Mutual Information
- Weighted Conditional Mutual Information
Note: all functions are calculated in log base 2, so return units of "bits".
y = [1 1 1 0 0]'; x = [1 0 1 1 0]';
mi(x,y) %% mutual information I(X;Y) ans = 0.0200
h(x) %% entropy H(X) ans = 0.9710
condh(x,y) %% conditional entropy H(X|Y) ans = 0.9510
h( [x,y] ) %% joint entropy H(X,Y) ans = 1.9219
joint([x,y]) %% joint random variable XY ans = 1 2 1 3 4
To compile the library for use in MATLAB/OCTAVE, execute CompileMIToolbox.m from within MATLAB, or run 'make matlab' from a terminal.
To compile the library for use with C programs run 'make x86' for a 32-bit library, or 'make x64' for a 64-bit library. Then run 'sudo make install' to install MIToolbox into /usr/local/lib & /usr/local/include.
The C source files are licensed under the LGPL v3. The MATLAB wrappers and demonstration feature selection algorithms are provided as is with no warranty as examples of how to use the library in MATLAB.
- 02/02/2015 - v2.1.1 - Fixed up the Makefile so it installs the headers too.
- 22/02/2014 - v2.1 - Fixed a couple of bugs related to memory handling. Added a make install for compatibility with PyFeast.
- 30/07/2011 - v2.00 - Added implementations of the weighted entropy and weighted mutual information. More cleanup of Mex entry point to further check the inputs.
- 08/11/2011 - v1.03 - Minor documentation changes to accompany the JMLR publication.
- 15/10/2010 - v1.02 - Fixed bug where MIToolbox would cause a segmentation fault if a x by 0 empty matrix was passed in. Now prints an error message and returns gracefully.
- 02/09/2010 - v1.01 - Fixed a bug in CMIM.m where the last feature would not be selected first if it had the highest MI.
- 07/07/2010 - v1.00 - Initial Release.