Struct DefaultCompare 

pub struct DefaultCompare;

Trait Implementations

impl Compare<f32> for DefaultCompare

fn dot(vec: impl Iterator<Item = f32>, other: impl Iterator<Item = f32>) -> f64

dot積 d(a, b) = Σ(a_i * b_i)

fn cosine_similarity( vec: impl Iterator<Item = (usize, f32)>, other: impl Iterator<Item = (usize, f32)>, ) -> f64

コサイン類似度 cos(θ) = Σ(a_i * b_i) / (||a|| * ||b||) ||a|| = sqrt(Σ(a_i^2))

fn chebyshev_distance( vec: impl Iterator<Item = f32>, other: impl Iterator<Item = f32>, ) -> f64

チェビシェフ距離 d(a, b) = max(|a_i - b_i|)

fn euclidean_distance( vec: impl Iterator<Item = f32>, other: impl Iterator<Item = f32>, ) -> f64

ユークリッド距離 d(a, b) = sqrt(Σ((a_i - b_i)^2))

fn manhattan_distance( vec: impl Iterator<Item = f32>, other: impl Iterator<Item = f32>, ) -> f64

マンハッタン距離 d(a, b) = Σ(|a_i - b_i|)

impl Compare<f64> for DefaultCompare

fn dot(vec: impl Iterator<Item = f64>, other: impl Iterator<Item = f64>) -> f64

dot積 d(a, b) = Σ(a_i * b_i)

fn cosine_similarity( vec: impl Iterator<Item = (usize, f64)>, other: impl Iterator<Item = (usize, f64)>, ) -> f64

コサイン類似度 cos(θ) = Σ(a_i * b_i) / (||a|| * ||b||) ||a|| = sqrt(Σ(a_i^2))

fn euclidean_distance( vec: impl Iterator<Item = f64>, other: impl Iterator<Item = f64>, ) -> f64

ユークリッド距離 d(a, b) = sqrt(Σ((a_i - b_i)^2))

fn manhattan_distance( vec: impl Iterator<Item = f64>, other: impl Iterator<Item = f64>, ) -> f64

マンハッタン距離 d(a, b) = Σ(|a_i - b_i|)

fn chebyshev_distance( vec: impl Iterator<Item = f64>, other: impl Iterator<Item = f64>, ) -> f64

チェビシェフ距離 d(a, b) = max(|a_i - b_i|)

impl Compare<u16> for DefaultCompare

fn euclidean_distance( vec: impl Iterator<Item = u16>, other: impl Iterator<Item = u16>, ) -> f64

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fn manhattan_distance( vec: impl Iterator<Item = u16>, other: impl Iterator<Item = u16>, ) -> f64

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fn chebyshev_distance( vec: impl Iterator<Item = u16>, other: impl Iterator<Item = u16>, ) -> f64

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fn dot(vec: impl Iterator<Item = u16>, other: impl Iterator<Item = u16>) -> f64

dot積 d(a, b) = Σ(a_i * b_i)

fn cosine_similarity( vec: impl Iterator<Item = (usize, u16)>, other: impl Iterator<Item = (usize, u16)>, ) -> f64

コサイン類似度 cos(θ) = Σ(a_i * b_i) / (||a|| * ||b||) ||a|| = sqrt(Σ(a_i^2))

impl Compare<u32> for DefaultCompare

fn dot(vec: impl Iterator<Item = u32>, other: impl Iterator<Item = u32>) -> f64

dot積 d(a, b) = Σ(a_i * b_i)

fn cosine_similarity( vec: impl Iterator<Item = (usize, u32)>, other: impl Iterator<Item = (usize, u32)>, ) -> f64

コサイン類似度 cos(θ) = Σ(a_i * b_i) / (||a|| * ||b||) ||a|| = sqrt(Σ(a_i^2))

fn euclidean_distance( vec: impl Iterator<Item = u32>, other: impl Iterator<Item = u32>, ) -> f64

ユークリッド距離 d(a, b) = sqrt(Σ((a_i - b_i)^2))

fn manhattan_distance( vec: impl Iterator<Item = u32>, other: impl Iterator<Item = u32>, ) -> f64

マンハッタン距離 d(a, b) = Σ(|a_i - b_i|)

fn chebyshev_distance( vec: impl Iterator<Item = u32>, other: impl Iterator<Item = u32>, ) -> f64

チェビシェフ距離 d(a, b) = max(|a_i - b_i|)

impl Compare<u8> for DefaultCompare

impl Compare for u8, u16, u32, f32, f64

fn dot(vec: impl Iterator<Item = u8>, other: impl Iterator<Item = u8>) -> f64

dot積 d(a, b) = Σ(a_i * b_i)

fn cosine_similarity( vec: impl Iterator<Item = (usize, u8)>, other: impl Iterator<Item = (usize, u8)>, ) -> f64

コサイン類似度 cos(θ) = Σ(a_i * b_i) / (||a|| * ||b||) ||a|| = sqrt(Σ(a_i^2))

fn euclidean_distance( vec: impl Iterator<Item = u8>, other: impl Iterator<Item = u8>, ) -> f64

ユークリッド距離 d(a, b) = sqrt(Σ((a_i - b_i)^2))

fn manhattan_distance( vec: impl Iterator<Item = u8>, other: impl Iterator<Item = u8>, ) -> f64

マンハッタン距離 d(a, b) = Σ(|a_i - b_i|)

fn chebyshev_distance( vec: impl Iterator<Item = u8>, other: impl Iterator<Item = u8>, ) -> f64

チェビシェフ距離 d(a, b) = max(|a_i - b_i|)

impl Debug for DefaultCompare

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

Formats the value using the given formatter.