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-====
PyFeast
====
-Python Interface to the FEAST Feature Selection Toolbox
+Python bindings to the FEAST Feature Selection Toolbox
-About
-====
-This set of scripts provides an interface to the FEAST feature selection
-toolbox, originally written in C with a Mex interface to Matlab. Python
-2.7 is required, along with Numpy. The feast.py module provides an inter-
-face to all the functionality of the FEAST implementation that was provided
-with the original Matlab interface.
+About PyFeast
+=============
+
+PyFeast is a interface for the FEAST feature selection toolbox, which was
+originally written in C with a interface to matlab.
+
+Because Python is also commonly used in computational science, writing bindings
+to enable researchers to utilize these feature selection algorthms in Python
+was only natural.
+
+At Drexel University's EESI Lab (link), we are using PyFeast to create a feature
+selection tool for the Department of Energy's upcoming KBase platform.
+
+
+Requirements
+============
+In order to use the feast module, you will need the following dependencies
+
+* Python 2.7
+* Numpy
+* Linux or OS X
Installation
-====
+============
To install the FEAST interface, you'll need to build and install the libraries
first, and then install python.
@@ -35,7 +48,16 @@ $ sudo python ./setup.py install
Demonstration
-====
+=============
See test/test.py for an example with uniform data and an image
data set. The image data set was collected from the digits example in
the Scikits-Learn toolbox.
+
+References
+==========
+* [FEAST](http://www.cs.man.ac.uk/~gbrown/fstoolbox/)
+ - The Feature Selection Toolbox
+* [Fizzy](http://www.kbase.us/developer-zone/api-documentation/fizzy-feature-selection-service/)
+ - A KBase Service for Feature Selection
+* [Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection]
+(http://jmlr.csail.mit.edu/papers/v13/brown12a.html)