Struct str_distance::token::TokenSort

pub struct TokenSort<D: DistanceMetric> { /* fields omitted */ }

TokenSort modifies the inner string distance dist to adjust for differences in word orders by reording words alphabetically.

For other types than strings this is just a delegate to the inner metric.

Trait Implementations

impl<D> DistanceMetric for TokenSort<D> where
    D: DistanceMetric, 

type Dist = <D as DistanceMetric>::Dist

Represents the data type in which this distance is evaluated.

fn distance<S, T>(&self, a: S, b: T) -> Self::Dist where
    S: IntoIterator,
    T: IntoIterator,
    <S as IntoIterator>::IntoIter: Clone,
    <T as IntoIterator>::IntoIter: Clone,
    <S as IntoIterator>::Item: PartialEq + PartialEq<<T as IntoIterator>::Item>,
    <T as IntoIterator>::Item: PartialEq, 

Generic implementation of the metric.

fn str_distance<S, T>(&self, a: S, b: T) -> Self::Dist where
    S: AsRef<str>,
    T: AsRef<str>, 

Evaluates the distance between two str.

fn normalized<S, T>(&self, a: S, b: T) -> f64 where
    S: IntoIterator,
    T: IntoIterator,
    <S as IntoIterator>::IntoIter: Clone,
    <T as IntoIterator>::IntoIter: Clone,
    <S as IntoIterator>::Item: PartialEq + PartialEq<<T as IntoIterator>::Item>,
    <T as IntoIterator>::Item: PartialEq, 

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.

fn str_normalized<S, T>(&self, a: S, b: T) -> f64 where
    S: AsRef<str>,
    T: AsRef<str>, 

Convenience normalization for str types.