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+MIToolbox v1.03 for C/C++ and MATLAB/OCTAVE
+
+The 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 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.
+
+Things you can do:
+ - Entropy
+ - 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
+
+Note: all functions are calculated in log base 2, so return units of "bits".
+
+======
+
+Examples:
+
+>> 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 C/C++, run 'make' at a terminal.
+
+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.
+
+Update History
+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 - Updated CMIM.m in demonstration_algorithms, due to a bug where the last feature would not be selected first if it had the highest MI
+07/07/2010 - v1.00 - Initial Release