dsimcha wrote:
We've got the beginnings of a good matrix/numerics lib in your work. I believe
that my dstats lib would provide just about all the statistical functionality a
good scientific lib would need, and I'm getting close to declaring it
beta-quality. I'd say based on a quick look at what scipy has that we also need
the following for a credible full-fledged scientific lib:
dstats has actually been kind of an inspiration for me. I've never used
it, because I don't do any statistics, but I've looked through the API
docs and the examples, and I really like the interface.
Machine learning, i.e. classification and clustering. (I'd probably be
qualified
to write that and was halfway thinking of breaking ground on it over Christmas
break. However, I'm not committing to this yet, so if someone else already has
a
work in progress, let me know. Also, there's a Java machine learning library
called Weka that I've used before. It's very complete but the API is painful
and
it's GPL, so IDK if it'd be worth it to port to D.)
Optimization. (A few people have tried but IDK if they've actually gotten far
off
the ground with it.)
Basic image I/O and processing.
Plotting. I've considered doing this a few times, but I've decided it needs to
be
put off until D2 is stable and the GUI toolkits for it are reasonably stable.
One
layer of instability (D2 itself) is workable, but two layers (D2 and the GUI
libs)
is not.
A few things that can be added to the list:
- special functions
- Fourier transforms
- Monte Carlo methods
- series
- least-squares fitting
- differential and integral equations
There is a good chance that I will need the latter for work just after
the holidays, in which case I'll write it myself.
I'll add the above lists to the project home page.
-Lars
