Mmm forking is usually considered harmful in contexts where the original
project is under development because of the difficulty to reincorporate
patches.
In your case I believe the HMM code is unmaintained and labelled as
deprecated in the user docs, so I don't think it's a real issue.
On the methodology side, here is how I would approach this:
1/ write up the goal of the refix (e.g. API conformance, performance, etc)
2/ share it with the ml so that others can shed light on use cases you
can't always imagine and their relative importance
3/ write (or locate/fix/rewrite if they exist) a battery of tests that can
be used to prove the fork is >= of the original (think algorithmic
accuracy, performance, ...)
4/ go with the refix in a [WIP] PR so that interested parties can give you
feedback very early on (it is not a pb I think of having a long lasting
branch)
5/ iterate until satisfied and change the PR to [MRG]
Enjoy :)
Eustache
2013/8/29 David Reed <david.ree...@gmail.com>
> Hi there,
>
> I want to have a go at fixing up the HMM toolbase. Its going to be
> something I work on independently for awhile before I feel comfortable
> asking for a pull request but wanted to get some advice on some best
> practices.
>
> Currently I have sklearn installed under my Anaconda directory. I think I
> want to do something like this:
>
> - create an hmm2.py file next to hmm.py under the site-packages directory.
>
> - Slowly make changes to hmm2.py.
> - having hmm2.py under site-packages, allows me to live with hmm2 and find
> out where it breaks, etc.
>
> This method works, but it doesn't seem like a common developmental
> process. Can someone suggest a better idea?
>
> Thanks for the help.
>
> -Dave
>
>
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