Haskell code is expressive. The HLearn library uses 6 lines of Haskell to define a function for training a Bayesian classifier; the equivalent code in the Weka library uses over 100 lines of Java. That’s a big difference! In this post, we’ll look at the actual code and see why the Haskell is so much more concise.
But first, a disclaimer: It is really hard to fairly compare two code bases this way. In both libraries, there is a lot of supporting code that goes into defining each classifier, and it’s not obvious what code to include and not include. For example, both libraries implement interfaces to a number of probability distributions, and this code is not contained in the source count. The Haskell code takes more advantage of this abstraction, so this is one language-agnostic reason why the Haskell code is shorter. If you think I’m not doing a fair comparison, here’s some links to the full repositories so you can do it yourself:
- HLearn’s bayesian classifier source code (74 lines of code)
- Weka’s naive bayes source code (946 lines of code)
In this post, we’re going to look at how to manipulate multivariate distributions in Haskell’s 
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