Crate eddie

Fast and well-tested implementations of edit distance/string similarity metrics:

• Levenshtein,
• Damerau-Levenshtein,
• Hamming,
• Jaro,
• Jaro-Winkler.

Usage

Cargo.toml:

[dependencies]
eddie = "0.2"

Levenshtein:

use eddie::Levenshtein;
let lev = Levenshtein::new();
let dist = lev.distance("martha", "marhta");
assert_eq!(dist, 2);

Damerau-Levenshtein:

use eddie::DamerauLevenshtein;
let damlev = DamerauLevenshtein::new();
let dist = damlev.distance("martha", "marhta");
assert_eq!(dist, 1);

Hamming:

use eddie::Hamming;
let hamming = Hamming::new();
let dist = hamming.distance("martha", "marhta");
assert_eq!(dist, Some(2));

Jaro:

use eddie::Jaro;
let jaro = Jaro::new();
let sim = jaro.similarity("martha", "marhta");
assert!((sim - 0.94).abs() < 0.01);

Jaro-Winkler:

use eddie::JaroWinkler;
let jarwin = JaroWinkler::new();
let sim = jarwin.similarity("martha", "marhta");
assert!((sim - 0.96).abs() < 0.01);

Complementary metrics

The main metric methods are complemented with inverted and/or relative versions. The naming convention across the crate is following:

• distance — a number of edits required to transform one string to the other;
• rel_dist — a distance between two strings, relative to string length (inversion of similarity);
• similarity — similarity between two strings (inversion of relative distance).

Performance

At the moment Eddie has the fastest implementations among the alternatives from crates.io that have Unicode support. For any given algorithm you can expect at least 1.5-2x speedup for typical use cases.

For the detailed measurements tables see GitHub page.

Structs

DamerauLevenshtein	Damerau-Levenshtein distance.
Hamming	Hamming distance.
Jaro	Jaro similarity.
JaroWinkler	Jaro-Winkler similarity.
Levenshtein	Levenshtein distance.