For the last couple of days, I’ve been thinking about what I wrote two weeks ago regarding SciRuby and the whole Ruby scientific computing scene. I still believe that the
sciruby gem can be used as an integrated environment, but there are some problems that must be solved before:
- We need a reasonably feature complete and easy to install version of NMatrix.
- A good plotting tool. Right now, Naoki is working on this as part of GSoC 2014.
- Statistics. Lots of things are already implemented in Statsample, but both
Statsample::Vectorshould use NMatrix behind the hood. Supporting JRuby can be problematic here…
- Given 1 and 2, it’s possible to implement a lot of other interesting and useful things. For example: linear regression methods, k-means clustering, neural networks, use NMatrix as a matrix type for OpenCV images. There are lots of possibilities.
- Minimization, integration and others.
With that in mind, my objective for the following weeks is to improve NMatrix. First, there are BLAS routines (mainly from level II, but some stuff from level I and III as well) that aren’t implemented in NMatrix and/or that aren’t available for the rational and ruby objects dtypes. There’s also LAPACK.
Another benefit of having complete C/C++ implementations is that we’ll eventually have to generalize these interfaces to allow other implementations (e.g. Mac OSX vecLib’s LAPACK, Intel’s MKL), thus making it much easier to install NMatrix. As Collin (and, I think, Pjotr) said in the sciruby-dev mailing list, it should be as easy as
gem install nmatrix.
BLAS and LAPACK general implementations
HAVE_CBLAS_Hbeing derived from
- Many more routines are implemented. Ideally, BLAS level 1 and 2 should be complete by the end of May.
An important next step is to be able to link against arbitrary BLAS and LAPACK implementations, given that they obey the standard. Issue #188 started some ideas; issue #22 is the original (and very old) one.
When NMatrix support both BLAS and LAPACK without a problem — i.e. have its own implementation and can also link against arbitrary ones (OSX’s vecLib, GSL, ATLAS, Intel’s MKL, AMD’s Core Math Library) — we’ll be able to build on top of it. There are some routines in NMatrix that are already working with every dtype, but most of them aren’t. When we know exactly which routines can’t work with which dtypes, we’ll reach a very good standpoint to talk about what we support.
Alright, we have determinants for rational matrices, but not “other operation”, etc. What else? STYPES! We also need to have good support for Yale matrices. (obs: maybe add “old Yale” format?)
There isn’t much to do: we have to support the whole BLAS/LAPACK standard, almost everything linear algebra-wise is in these. After that, it’s mostly improvements to the interface, better method naming, better documentation and examples, better IO, etc.
Another point that would be good to adress is to remove the dependency of g++ > 4.6. We should strive to remove everything that depends on C++11 features, thus allowing normal Mac OSX users to install NMatrix without having to first install another compiler.
We need to refactor our documentation. Oh, how we need to!
First, remove everything that shouldn’t be in the facing API — the classes and modules used in NMatrix::IO shouldn’t be available in the public API anyway, only the outside-facing stuff: how to save and load to/from each format. Probably more things as well.
Second, do a better job of being consistent with docs. There are some methods without a return type or stuff like that. Lots of methods in the C/C++ world aren’t documented as well. We can do better!
Finally, a really good documentation template. Fivefish is a good choice — it provides a very pretty, searchable and clean interface. (create NMatrix’s docs with it and host on my own server, see what happens).