Code for this entry: here. In this first part, we started shamelessly plagiarizing creatively reproducing a Clojure monad that does a great job describing stochastically independent processes. After a few trials I became convinced, that the approach in the blog post mentioned above was indeed optimal: a “natural” approach of modelling an event as a […]

The idea for doing this is not new. There is an excellent series of posts closely tracing an article on applications of functional programming to probability. A colleague of mine has recently called my attention to his own post of two years ago, where he describes a monad that models stochastically independent events in Clojure. […]

Marching on from the last post. Lazy Sequences This is my favorite feature ever. If I want to generate just a few of 10! (nobody even knows how much that is) permutations, I could: provided, the function is defined (as described in the first post): Here I am not sure which language I like more. […]

The Alogirthm Recently, I have entered a brave (new?) world of Clojure and was looking for a small project to take it for a ride. I stopped on a popular/interesting enough little problem, that subsumed a certain interview question which I was once unfortunate enough to stumble through with half my brain tied behind my […]

Compute the mathces Here is a simple, purely brute-force algorithm for computing the join mentioned in Part 1. Here is the entirely “CPU” implementation of the algorithm: Loop over both datasets, compare them one-by-one, if there is a match – flag it. The only thing to note is that while it’s tempting to write the […]