strsim-rs

Rust implementations of string similarity metrics:

Hamming
Levenshtein - distance & normalized
Optimal string alignment
Damerau-Levenshtein - distance & normalized
Jaro and Jaro-Winkler - this implementation of Jaro-Winkler does not limit the common prefix length

The normalized versions return values between 0.0 and 1.0, where 1.0 means an exact match.

There are also generic versions of the functions for non-string inputs.

Installation

strsim is available on crates.io. Add it to your Cargo.toml:

[dependencies]
strsim = "0.9.1"

Usage

Go to Docs.rs for the full documentation. You can also clone the repo, and run $ cargo doc --open.

Examples

extern crate strsim;

use strsim::{hamming, levenshtein, normalized_levenshtein, osa_distance,
             damerau_levenshtein, normalized_damerau_levenshtein, jaro,
             jaro_winkler};

fn main() {
    match hamming("hamming", "hammers") {
        Ok(distance) => assert_eq!(3, distance),
        Err(why) => panic!("{:?}", why)
    }

    assert_eq!(levenshtein("kitten", "sitting"), 3);

    assert!((normalized_levenshtein("kitten", "sitting") - 0.571).abs() < 0.001);

    assert_eq!(osa_distance("ac", "cba"), 3);

    assert_eq!(damerau_levenshtein("ac", "cba"), 2);

    assert!((normalized_damerau_levenshtein("levenshtein", "löwenbräu") - 0.272).abs() <
            0.001);

    assert!((jaro("Friedrich Nietzsche", "Jean-Paul Sartre") - 0.392).abs() <
            0.001);

    assert!((jaro_winkler("cheeseburger", "cheese fries") - 0.911).abs() <
            0.001);
}

Using the generic versions of the functions:

extern crate strsim;

use strsim::generic_levenshtein;

fn main() {
    assert_eq!(2, generic_levenshtein(&[1, 2, 3], &[0, 2, 5]));
}

Contributing

If you don't want to install Rust itself, you can run $ ./dev for a development CLI if you have Docker installed.

Benchmarks require a Nightly toolchain. Run $ cargo +nightly bench.

License

MIT

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