[SciPy-user] Where is the border between SciPy and ML algorithms?

David Cournapeau david@ar.media.kyoto-u.ac...
Thu Nov 8 03:12:19 CST 2007


Willi Richert wrote:
> Hi,
>
> the scipy.cluster module up to now contains already algorithms for Vector 
> Quantization and Kmeans. In the docs it's said that SOMs are following. Now, 
> after having followed the mails mentioning Orange and Elefant: Where do we 
> draw the line?
>   
My understanding is that we do not throw everything we can think of in 
scipy; scipy.cluster was kept  in scipy for backward compatibility ? 
That being said, I don't remember having seen any mention of line 
between core and non core algorithms.

Maybe the main difference between scikits and scipy is the license: 
whereas scipy avoids any non BSD -like license, scikits is freeer in 
this respect (you can depend on GPL code, for example).

> I'm asking, because a student of mine has implemented SWIG-Python-wrappers to 
> the C++ implementations of diverse decision trees (continuous VFDT, C4.5 and 
> some others), which work quite well. We would of course be glad if those 
> would be included in standard SciPy. However, I'm not quite sure, what the 
> current policy of the core SciPy developers is regarding non-core algorithms.
>   
If this is under open source license, as Matthieu said, we (I) can 
import it into the learn scikits, which implements already several 
machine learning algorithms (EM for Gaussian mixtures, datasets, SVM).

cheers,

David


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