diff options
Diffstat (limited to 'README.markdown')
| -rw-r--r-- | README.markdown | 31 |
1 files changed, 8 insertions, 23 deletions
diff --git a/README.markdown b/README.markdown index e2222a7..86aa0f2 100644 --- a/README.markdown +++ b/README.markdown @@ -10,7 +10,8 @@ to enable researchers to utilize these feature selection algorithms in Python was only natural. At Drexel University's [EESI Lab](http://www.ece.drexel.edu/gailr/EESI/), we are using PyFeast to create a feature -selection tool for the Department of Energy's upcoming KBase platform. +selection tool for the Department of Energy's upcoming KBase platform. We are also integrating a tool that utilizes +PyFeast as a script for Qiime users: [Qiime Fizzy Branch](https://github.com/EESI/FizzyQIIME) ## Requirements In order to use the feast module, you will need the following dependencies @@ -18,28 +19,10 @@ In order to use the feast module, you will need the following dependencies * Python 2.7 * Numpy * Linux or OS X +* [FEAST](https://github.com/Craigacp/FEAST) +* [MIToolbox](https://github.com/Craigacp/MIToolbox) ## Installation -To install the FEAST interface, you'll need to build and install the FEAST -libraries first, and then install python. - -Make MIToolbox and install it: - - cd FEAST/MIToolbox - make - sudo make install - -Make FSToolbox and install it: - - cd FEAST/FSToolbox - make - sudo make install - -Run ldconfig to update your library cache: - - sudo ldconfig - -Install our PyFeast module: python ./setup.py build sudo python ./setup.py install @@ -49,8 +32,10 @@ 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. +## Documentation +We have documentation for each of the functions available [here](http://mutantturkey.github.com/PyFeast/feast-module.html) + ## 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) +* [Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection](http://jmlr.csail.mit.edu/papers/v13/brown12a.html) |