This library does not calculate the edit distance.
Rather, it provides extremely efficient automata that answer whether 2 values are within a predetermined number of edits of one another. Edits are: additions, deletions, replacements.
Once generated, an automaton can be reused to compare any 2 values in around 2-8 µs.
Published: 11 Jul 2023
Extremely fast Levenshtein comparator.
Checks whether 2 values are within a predetermined number of edits of one another.
most comparisons take under 5 µs, depending on the length of the values
a Functor is provided to enable comparisons across any arbitrary types
string comparisons are provided (functorized) out of the box
reuse the same automaton across all comparisons with the same
max_edits, regardless of the type of the values being compared
max_editsmust be between
3(inclusively) due to the astronomical scaling factor during graph building
(* Create an automaton *) let within3 = Fuzzy_compare.create ~max_edits:3 in (* true *) let b1 = Fuzzy_compare.String.eval within3 "kitten" "kitsch" in (* false *) let b2 = Fuzzy_compare.String.eval within3 "kittens" "kitsch" in
Unicode / arbitrary types
This example demonstrates how to compare unicode string as well as how to compare your own types.
For unicode we'll need
opam install uunf uuseg
First apply the
(* Fuzzy_compare.Make is documented in [fuzzy_compare.mli] *) module Utf8 = Fuzzy_compare.Make (struct type t = string type cmp = string [@@deriving equal] type index = string array let index x = (* Normalize to Unicode NFC, then break the string into an array of Grapheme Clusters *) Uunf_string.normalize_utf_8 `NFC x |> Uuseg_string.fold_utf_8 `Grapheme_cluster (fun acc s -> s :: acc)  |> Array.of_list_rev let length = Array.length let get = Array.get let fold = Array.fold end)
Then proceed as usual:
let within2 = Fuzzy_compare.create ~max_edits:2 in (* false because each emoji is made of multiple bytes *) let b1 = Fuzzy_compare.String.eval within2 "🏁🏴🚩" "🚩🏴🏁" in (* true thanks to Unicode awareness *) let b1 = Utf8.eval within2 "🏁🏴🚩" "🚩🏴🏁" in (* false *) let b2 = Utf8.eval within2 "🏁🏴🚩" "🚩🏳️" in
Thanks to Paul Masurel and Jules Jacob for explaining clearly the beauty of the algorithms contained within the "Fast String Correction with Levenshtein-Automata" paper, and thanks to Klaus Schulz and Stoyan Mihov for writing the paper.
This library implements the most sophisticated (and most efficient) version described by Paul Masurel, with several additional low level optimizations.
Recommended reading: https://julesjacobs.com/2015/06/17/disqus-levenshtein-simple-and-fast.html https://fulmicoton.com/posts/levenshtein/