[SciPy-dev] SciPy improvements

Bill Baxter wbaxter@gmail....
Fri Apr 13 00:41:10 CDT 2007

On 4/13/07, David Cournapeau <david@ar.media.kyoto-u.ac.jp> wrote:
> Bill Baxter wrote:
> > On 4/13/07, David Cournapeau <david@ar.media.kyoto-u.ac.jp> wrote:
> > I would be interested in joining a dev list on this or something like
> > that (or open dev blog? or wiki?) if you start such a thing.  I assume
> > you have to have discussions with your mentor anyway.  If possible
> > it'd be nice to peek in on those conversations.
> >
> There is nothing started yet, and some things need to be fixed with my
> mentor before things get started, but as Robert said, most if not all
> discussion related to it would happen here and follow the usual scipy
> process (scipy SVN, Trac, etc...).

Great then.

The project page mentions SVM.  In addition to SVM I'm interested in
things like PPCA, kernel PCA, RBF networks, gaussian processes and
GPLVM.  Are you going to try to go in the direction of a modular
structure with reusable bits for for all kernel methods, or is the
plan to targeted specifically SVM?

The basic components of this stuff (like RBFs) also make for good
scattered data interpolation schemes.  I hear questions every so often
on the list about good ways to do that, so making the tools for the
machine learning toolkit easy to use for people who just want to
interpolate data would be nice.

Going in a slightly different direction, meshfree methods for solving
partial differential equations also build on tools like RBF and moving
least squares interpolation.  So for that reason too, it would be nice
to have a reusable api layer for those things.

You mention also that you're planning to unify row vec vs. column vec
conventions.  Just wanted to put my vote in for row vectors!  For a
number of reasons
1) It seems to be the more common usage in machine learning literature
2) with Numpy's default C-contiguous data it puts individual vectors
in contiguous memory.
3) it's easier to print something that's Nx5 than 5xN
4) "for vec in lotsofvecs:" works with row vectors, but needs a
transpose for column vectors.
5) accessing a vector becomes just data[i] instead of data[:,i] which
makes it easier to go back and forth between a python list of vectors
and a numpy 2d array of vectors.


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