On Mon, 2011-03-21 at 20:27 -0500, Donnie Berkholz wrote:
> On 16:56 Mon 21 Mar     , Brian Dolbec wrote:
> > There is an app called shogun-toolbox  http://www.shogun-toolbox.org/
> > 
> > that is designed specifically for machine learning.  It has many 
> > interface capabilities including python (which they state has the most 
> > complete documentation).  There is also many examples for it's use.
> > 
> > I personally don't know nearly enough about it to know what modeling 
> > methods might be suitable or even if there is one suitable for this 
> > type of model.  But I think should be investigated.  Implemented this 
> > would have the potential to increase it's success rate over time, 
> > reducing developer load even more.
> 
> This sounds like something that might be beyond a GSoC project to 
> develop a standalone code base. It would probably work better next year 
> as an enhancement, if this year's project to build an initial working 
> application worked out well.

Absolutely.  But I wanted to get the thought out there.  Then maybe this
years code could be designed to be easier to upgrade it's AI next year.


> 
> What I would do instead is a "smart" detection using things like 
> flex/bison/pybison. You could first detect the buildsystem type 
> (autotools/cmake/distutils/etc) based on file existence. Then you would 
> parse the build files using the syntax described for your lexical 
> analyzer/parser to build a basic understanding of what build-time 
> options are available and offer them as USE flags. You could also do 
> similar things to detect dependencies by understanding their syntax in 
> the source code.
> 

Yes there are several areas that a machine learning system could be
beneficial in this whole process.  Some of which are listed as other
project ideas this year.

-- 
Brian Dolbec <brian.dolbec@...>