From 5ab6b8a806e1974b187f9677f95fdc9c4853bf34 Mon Sep 17 00:00:00 2001
From: Calvin <calvin@EESI>
Date: Tue, 2 Apr 2013 12:09:15 -0400
Subject: fix references and use other style of headers, make all header H2 but
 first one.

---
 README.markdown | 25 +++++++++----------------
 1 file changed, 9 insertions(+), 16 deletions(-)

diff --git a/README.markdown b/README.markdown
index 2b731c8..bb50aae 100644
--- a/README.markdown
+++ b/README.markdown
@@ -1,9 +1,8 @@
-PyFeast
-====
+# PyFeast
+
 Python bindings to the FEAST Feature Selection Toolbox
 
-About PyFeast
-=============
+## About PyFeast
 
 PyFeast is a interface for the FEAST feature selection toolbox, which was
 originally written in C with a interface to matlab.
@@ -16,16 +15,14 @@ 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
-============
+## Requirements
 In order to use the feast module, you will need the following dependencies
 
 * Python 2.7
 * Numpy
 * Linux or OS X 
 
-Installation
-============
+## Installation
 To install the FEAST interface, you'll need to build and install the libraries 
 first, and then install python.
 
@@ -47,17 +44,13 @@ $ python ./setup.py build
 $ sudo python ./setup.py install
 
 
-Demonstration
-=============
+## 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
+## 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