Damn Cool Algorithms: Levenshtein Automata

In a previous Damn Cool Algorithms post, I talked about BK-trees, a clever indexing structure that makes it possible to search for fuzzy matches on a text string based on Levenshtein distance - or any other metric that obeys the triangle inequality. Today, I'm going to describe an alternative approach, which makes it possible to do fuzzy text search in a regular index: Levenshtein automata.


The basic insight behind Levenshtein automata is that it's possible to construct a Finite state automaton that recognizes exactly the set of strings within a given Levenshtein distance of a target word. We can then feed in any word, and the automaton will accept or reject it based on whether the Levenshtein distance to the target word is at most the distance specified when we constructed the automaton. Further, due to the nature of FSAs, it will do so in O(n) time with the length of the string being tested. Compare this to the standard Dynamic Programming Levenshtein algorithm, which takes O(mn) time, where m and n are the lengths of the two input words! It's thus immediately apparrent that Levenshtein automaton provide, at a minimum, a faster way for ...

Getting unicode right in Python

Yup, I'm back from holidays! Apologies to everyone for the delayed return - it's taking me a long while to catch up on everything that built up while I was away.

Proper text processing - specifically, correct handling of unicode - is one of those things that consistently confounds even experienced developers. This isn't because it's difficult, but rather, I believe, because most developers carry around a few key misconceptions about what text (in the context of software) is and how it's represented. A search on StackOverflow for UnicodeDecodeError demonstrates just how prevalent these misconceptions are. These misconceptions date back to the days before unicode - longer than many developers have been in the industry, including myself - but they're still nothing if not widespread. This is in part because a number of well known and popular languages continue to, at worst, perpetuate the misunderstandings, and at best are insufficiently good at helping developers get it right.

We can divide languages into four categories along the axis of unicode support:

  1. Languages that were written before unicode was defined, or widespread. C and C++ fall into this category. Languages in this category tend to have unicode support that's spotty ...