Crate str_distance

Compute distances between strings types (and others)

This crate provides implementations for a variety of distance or equality metrics. When using metrics that are a measure of similarity, the following should be noted: All implementations always return the value of the distance between two elements (e.g. str), i.e. their degree of dissimilarity. Which the implemented metrics that are designed to measure similarity (e.g. Jaccard index) will return the distance, which is complementary to the similarity score.

Usage

The str_distance::str_distance* convenience functions.

str_distance and str_distance_normalized take the two string inputs for which the distance is determined using the passed ’DistanceMetric. str_distance_normalized` evaluates the normalized distance between two strings. A value of ‘0.0’ corresponds to the “zero distance”, both strings are considered equal by means of the metric, whereas a value of ‘1.0’ corresponds to the maximum distance that can exist between the strings.

Calling the str_distance::str_distance* is just convenience for DistanceMetric.str_distance*("", "")

Example

Levenshtein metrics offer the possibility to define a maximum distance at which the further calculation of the exact distance is aborted early.

Distance

use str_distance::*;

// calculate the exact distance
assert_eq!(str_distance("kitten", "sitting", Levenshtein::default()), DistanceValue::Exact(3));

// short circuit if distance exceeds 10
let s1 = "Wisdom is easily acquired when hiding under the bed with a saucepan on your head.";
let s2 = "The quick brown fox jumped over the angry dog.";
assert_eq!(str_distance(s1, s2, Levenshtein::with_max_distance(10)), DistanceValue::Exceeded(10));

Normalized Distance

use str_distance::*;
assert_eq!(str_distance_normalized("" , "", Levenshtein::default()), 0.0);
assert_eq!(str_distance_normalized("nacht", "nacht", Levenshtein::default()), 0.0);
assert_eq!(str_distance_normalized("abc", "def", Levenshtein::default()), 1.0);

The DistanceMetric trait

use str_distance::{DistanceMetric, SorensenDice};
// QGram metrics require the length of the underlying fragment length to use for comparison.
// For `SorensenDice` default is 2.
assert_eq!(SorensenDice::new(2).str_distance("nacht", "night"), 0.75);

DistanceMetric was designed for str types, but is not limited to. Calculating distance is possible for all data types which are comparable and are passed as ‘IntoIterator’, e.g. as Vec or slice

use str_distance::{DistanceMetric, Levenshtein, DistanceValue};

assert_eq!(*Levenshtein::default().distance(&[1,2,3], &[1,2,3,4,5,6]),3);

Re-exports

pub use jaro::Jaro;

pub use jaro::JaroWinkler;

pub use levenshtein::DamerauLevenshtein;

pub use levenshtein::Levenshtein;

pub use modifiers::Winkler;

pub use modifiers::WinklerConfig;

pub use qgram::Cosine;

pub use qgram::Jaccard;

pub use qgram::Overlap;

pub use qgram::QGram;

pub use qgram::SorensenDice;

pub use ratcliff::RatcliffObershelp;

pub use token::TokenSet;

pub use token::TokenSort;

Modules

jaro

levenshtein

modifiers

qgram

ratcliff

token

Enums

DistanceValue

Traits

DistanceElement

Convenience trait to use a distance on a type directly.

DistanceMetric

Functions

str_distance

Evaluates the distance between two strings based on the provided crate::DistanceMetric.

str_distance_normalized

Evaluates the normalized distance between two strings based on the provided crate::DistanceMetric, so that it returns always a f64 between 0 and 1. A value of ‘0.0’ corresponds to the “zero distance”, both strings are considered equal by means of the metric, whereas a value of ‘1.0’ corresponds to the maximum distance that can exist between the strings.