Struct TokenSort

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

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.