I have been playing around with Clojure and after familiarising myself with the core, I decided to start writing little programs that forces me to choose the right idioms. I am starting off with Levenshtein edit distance of two strings.

Levenshtein edit distance

Levenshtein edit distance between two string is a way of quantifying how similar or dissimilar they are, lower distance implying higher similarity. The algorithm to calculate Levenshtein distance is fairly simple - the algorithm cares about three operations that can be performed at a particular position in a string to move it towards the other - insertion, deletion and substitution.

This can be recursively solved for two strings A0:AS and B0:BS by comparing AS, B0:BS; A0:AS, BS and AS, BS.

Code

(ns clojure_dojo.core)

(defn edit-distance
  "Return the Levenshtein edit distance between two strings"
  [first second]
  (cond
   (empty? first) (count second)
   (empty? second) (count first)
   :else (min (+ 1 (edit-distance (drop 1 first) second))
              (+ 1 (edit-distance first (drop 1 second)))
              (+ (cond
                  (= (take 1 first) (take 1 second)) 0
                  :else 1)
                 (edit-distance (drop 1 first) (drop 1 second))))))

Test

(ns clojure_dojo.t-core
  (:use midje.sweet)
  (:use [clojure_dojo.core]))

(facts "about `edit-distance`"
       (fact "it returns length of other string if one string is empty"
             (edit-distance "foo" "") => 3
             (edit-distance "" "foo") => 3)
       (fact "detects deletions"
             (edit-distance "foo" "oo") => 1)
       (fact "detects substitutions"
             (edit-distance "ab", "cd") => 2)
       (fact "detects additions needed"
             (edit-distance "oo", "foo") => 1
             (edit-distance "kit", "sitting") => 5
             (edit-distance "intention", "execution") => 5
             (edit-distance "sittin", "sitting") => 1))

The problem with this approach

This solution looks fine - it passes the tests. However, if we carefully observe the recursion tree, we notice that there are sub-problems that are solved multiple times and this makes this algorithm’s order of complexity Θ(3^min(m, n)). This can be observed from the call tree of this recursion.

Improvement

Recursive algorithms of this nature can be improved in two ways - memoization during recursion or applying Dynamic Programming in a bottom up manner. The next blog post in this series will deal with how these two approaches can be done in Clojure.

The code for the above solution is on GitHub.

If you have questions or comments about this blog post, you can get in touch with me on Twitter @sdqali.

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