Struct Result 

pub struct Result<R> { /* private fields */ }

Result of a distance/similarity algorithm.

Implementations

impl Result<usize>

pub fn val(&self) -> usize

Raw value of the metric.

It is equivalent to dist for distance metrics and to sim for similarity metrics.

use textdistance::{Algorithm, Hamming};
let h = Hamming::default();
let res = h.for_str("abc", "acbd");
assert!(res.val() == 3);

pub fn dist(&self) -> usize

Absolute distance.

A non-negative number showing how different the two sequences are. Two exactly the same sequences have the distance 0.

The highest possible number varies based on the length of the input strings. Most often, each increment of this value indicates one symbol that differs in the input sequences.

use textdistance::{Algorithm, Hamming};
let h = Hamming::default();
let res = h.for_str("abc", "acbd");
assert!(res.dist() == 3);

pub fn sim(&self) -> usize

Absolute similarity.

A non-negative number showing how similar the two sequences are. Two absolutely different sequences have the similarity 0.

The highest possible number varies based on the length of the input strings. Most often, each increment of this value indicates one symbol that is the same in both sequences.

use textdistance::{Algorithm, Hamming};
let h = Hamming::default();
let res = h.for_str("abc", "acbd");
assert!(res.sim() == 1); // "a"

pub fn nval(&self) -> f64

Normalized raw value of the metric.

It is equivalent to ndist for distance metrics and to nsim for similarity metrics.

use textdistance::{Algorithm, Hamming};
let h = Hamming::default();
let res = h.for_str("abc", "acbd");
assert!(res.nval() == 3.0 / 4.0);

pub fn ndist(&self) -> f64

Normalized distance.

A number from 0.0 to 1.0 showing how different the two sequences are. 0.0 indicates that the sequences are the same, and 1.0 indicates that the sequences are very different.

use textdistance::{Algorithm, Hamming};
let h = Hamming::default();
let res = h.for_str("abc", "acbd");
assert!(res.ndist() == 3.0 / 4.0);

pub fn nsim(&self) -> f64

Normalized similarity.

A number from 0.0 to 1.0 showing how similar the two sequences are. 0.0 indicates that the sequences are very different, and 1.0 indicates that the sequences are the same.

use textdistance::{Algorithm, Hamming};
let h = Hamming::default();
let res = h.for_str("abc", "acbd");
assert!(res.nsim() == 1.0 / 4.0);

impl Result<f64>

pub fn nval(&self) -> f64

Normalized raw value of the metric.

It is equivalent to ndist for distance metrics and to nsim for similarity metrics.

use textdistance::{Algorithm, Jaro};
let h = Jaro::default();
let res = h.for_str("test", "tset");
assert_eq!(res.nval(), 0.9166666666666666);

pub fn ndist(&self) -> f64

Normalized distance.

A number from 0.0 to 1.0 showing how different the two sequences are. 0.0 indicates that the sequences are the same, and 1.0 indicates that the sequences are very different.

use textdistance::{Algorithm, Jaro};
let h = Jaro::default();
let res = h.for_str("test", "tset");
assert_eq!(res.ndist(), 0.08333333333333337);

pub fn nsim(&self) -> f64

Normalized similarity.

A number from 0.0 to 1.0 showing how similar the two sequences are. 0.0 indicates that the sequences are very different, and 1.0 indicates that the sequences are the same.

use textdistance::{Algorithm, Jaro};
let h = Jaro::default();
let res = h.for_str("test", "tset");
assert_eq!(res.nsim(), 0.9166666666666666);