Monthly Archives: April 2014

Solving linear systems in NMatrix

I’m writing some guides for NMatrix, so in the following weeks there should be some posts similar to this one, but more complex.

Linear systems are one of the most useful methods from “common algebra”. Various problems can be represented by them: systems of linear ODEs, operations research optimizations, linear electrical circuits and a lot of the “wording problems” from basic algebra. We can represent these systems as

Ax = b

Where A is a matrix of coefficients and b a vector representing the other side of the equation.

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Gems for scientific computing

UPDATE (20/04): User centrx from #ruby-lang at freenode warned me that I forgot about RSRuby/RinRuby, so I added them to projects.yml.

In Wicked Good Ruby 2013, Bryan Liles made a presentation about Machine Learning with Ruby. It’s a good introduction to the subject and he presents some useful tricks (I didn’t know about Grapher.app, for example). But the best advise I could get is that there’s a lot of room for improvement in the Ruby scientific computing scene.

Having contributed to some SciRuby projects in the last year, I’ve seen it first-hand. With NMatrix, it’s possible to do a lot of vector and matrix calculations easily, if you know how to install it — a task that’s much easier today. There are statsample for statistics, distribution for probability distributions, minimization, integration, the GSL bindings and others. But if you need plotting, it can be pretty hard to use (e.g. Rubyvis) or depend on external programs (Plotrb outputs SVG files). Do you want an integrated environment, like MATLAB or Pylab? There isn’t one.

Searching for more instances of people interested in the subject, I found a presentation about neural networks by Matthew Kirk from Ruby Conf 2013, an Eurucamp 2013 presentation by Juanjo Bazán and slides from a presentation by Shahrooz Afsharipour at a German university. If we needed any confirmation that there are folks looking for SciRuby, here’s the evidence.

What can be done

In order to address these problems, I’m trying to come up with concrete steps towards creating a scientific community around Ruby. It’s obvious we need “more scientific libraries”, but what do we already have? What is easy to install and what isn’t? Should we create something new or improve what we have?

Also, I’m mapping the Ruby scientific computing landscape. I’ve compiled a YAML file with a list of the projects that I’ve found so far. In the future, this could be transformed in a nice visualization on sciruby.org to help scientists find the libraries they need.

If you know how to use the R programming language, both RSRuby and RinRuby can be used. They’re libraries that run R code inside of Ruby, so you can technically do anything you’d do with R in Ruby. This is suboptimal and R isn’t known for its speed.

For an integrated environment, we can revive the sciruby gem. For example:

I’m updating the SciRuby repository in this branch. Before creating the above DSL, it’s necessary to remove a lot of cruft (e.g. should use bundler/gem_tasks instead of hoe) and add some niceties (e.g. Travis CI support). Most importantly, adding dependency to the main SciRuby projects — NMatrix, statsample, minimization, integration, etc — in order to have a real integrated environment without require’ing everything manually. I’ll probably submit a pull request by next week.

We also need to improve our current selection: NMatrix installation shouldn’t depend on ATLAS, plotrb (or other solution) needs to be more usable, show how IRuby can be used to write scripts with nice graphics and LaTeX-support and create a list of all the applications that use our libraries for reference.

The Ruby Science Foundation was selected for Google Summer of Code 2014, thus some very bright students will help us fix some of these problems during the summer. However, there’s a lot to be done in every SciRuby project, if you’ve got the time. :)

Conclusion

We still have a long way before having a full-fledged scientific community — but there’s hope! Some areas to look at:

  • Good numerical libraries: NMatrix, mdarray.
  • Algorithms for data mining, modeling and simulations: AI4R, ruby-fann, ruby-libsvm, statsample, distribution, etc.
  • Plotting: Rubyvis is a port of Protovis, which was deprecated in favor of
    D3js. Thus, we should create some plotting library around a C backend or
    around D3, like Plotrb.
  • Integrated environment: IRuby together with SciRuby.

Except for plotting, an area that really needs a lot of love and care, most of these are already working, but with usability problems (installation, mostly).

If you think that it’d be cool to have a scientific community centered around Ruby and you do have some time available, please please please:

  1. Take a look at the SciRuby repositories.
  2. If there’s a subject you’re interested in, see if you can refactor something, add more tests, well, anything.
  3. Open issues about new features or pull requests improving the current ones.
  4. If you don’t understand much about the subject, but see something that could be improved, do it: is there Travis CI support? Something wrong with the gemspec? Is it still using some gem to generate gemspecs?
  5. There’s the sciruby-dev mailing list and the #sciruby channel on Freenode if there’s something you want to ask or discuss.

You can find me as agarie on freenode or @carlos_agarie on twitter.

References

  1. SciRuby. SiteGitHub
  2. List of scientific computing projects in Ruby. projects.yml
  3. Wicked Good Ruby 2013. Site
  4. Bryan Liles: Machine Learning with Ruby. bryan
  5. Matthew Kirk: Test-driven neural networks with Ruby. neural
  6. Shahrooz Afsharipour: Ruby in the context of scientific computing. slides in PDF
  7. Juanjo Bazán: presentation in Eurucamp 2013. juanjo-slides