From 261e825fcf865f0fb5097222a5d86543949764da Mon Sep 17 00:00:00 2001 From: Calvin Date: Tue, 2 Apr 2013 12:01:59 -0400 Subject: updated README with a improved about section, requirements, and references --- README.markdown | 44 +++++++++++++++++++++++++++++++++----------- 1 file changed, 33 insertions(+), 11 deletions(-) (limited to 'README.markdown') diff --git a/README.markdown b/README.markdown index cb46224..2b731c8 100644 --- a/README.markdown +++ b/README.markdown @@ -1,18 +1,31 @@ -==== 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) -- cgit v1.2.3