Enum SimilarityMetric 

pub enum SimilarityMetric {
    Cosine,
    Euclidean,
    Manhattan,
    DotProduct,
}

Similarity metrics for vector comparison

Different metrics are suitable for different use cases and vector characteristics.

Examples

use vectorlite::SimilarityMetric;

let a = vec![1.0, 2.0, 3.0];
let b = vec![1.1, 2.1, 3.1];
 
let cosine_score = SimilarityMetric::Cosine.calculate(&a, &b);
let euclidean_score = SimilarityMetric::Euclidean.calculate(&a, &b);

Variants

Cosine

Cosine similarity - scale-invariant, good for normalized embeddings Range: [-1, 1], where 1 is identical

Euclidean

Euclidean similarity - geometric distance converted to similarity Range: [0, 1], where 1 is identical

Manhattan

Manhattan similarity - L1 norm distance converted to similarity Range: [0, 1], where 1 is identical, robust to outliers

DotProduct

Dot product - raw similarity without normalization Range: unbounded, requires consistent vector scaling

Implementations

impl SimilarityMetric

pub fn calculate(&self, a: &[f64], b: &[f64]) -> f64

Trait Implementations

impl Clone for SimilarityMetric

fn clone(&self) -> SimilarityMetric

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SimilarityMetric

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for SimilarityMetric

fn default() -> Self

Returns the “default value” for a type.

impl PartialEq for SimilarityMetric

fn eq(&self, other: &SimilarityMetric) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Copy for SimilarityMetric

impl StructuralPartialEq for SimilarityMetric