//! # Euclidian Metric Distance //! //! The Euclidean distance (L2) between two points \\( x \\) and \\( y \\) in n-space is defined as //! //! \\[ d(x, y) = \sqrt{\sum_{i=1}^n (x-y)^2} \\] //! //! Example: //! //! ``` //! use smartcore::metrics::distance::Distance; //! use smartcore::metrics::distance::euclidian::Euclidian; //! //! let x = vec![1., 1.]; //! let y = vec![2., 2.]; //! //! let l2: f64 = Euclidian::new().distance(&x, &y); //! ``` //! //! //! #[cfg(feature = "serde")] use serde::{Deserialize, Serialize}; use std::marker::PhantomData; use crate::linalg::basic::arrays::ArrayView1; use crate::numbers::basenum::Number; use super::Distance; /// Euclidean distance is a measure of the true straight line distance between two points in Euclidean n-space. #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))] #[derive(Debug, Clone)] pub struct Euclidian { _t: PhantomData, } impl Default for Euclidian { fn default() -> Self { Self::new() } } impl Euclidian { /// instatiate the initial structure pub fn new() -> Euclidian { Euclidian { _t: PhantomData } } /// return sum of squared distances #[inline] pub(crate) fn squared_distance>(x: &A, y: &A) -> f64 { if x.shape() != y.shape() { panic!("Input vector sizes are different."); } let sum: f64 = x .iterator(0) .zip(y.iterator(0)) .map(|(&a, &b)| { let r = a - b; (r * r).to_f64().unwrap() }) .sum(); sum } } impl> Distance for Euclidian { fn distance(&self, x: &A, y: &A) -> f64 { Euclidian::squared_distance(x, y).sqrt() } } #[cfg(test)] mod tests { use super::*; #[cfg_attr( all(target_arch = "wasm32", not(target_os = "wasi")), wasm_bindgen_test::wasm_bindgen_test )] #[test] fn squared_distance() { let a = vec![1, 2, 3]; let b = vec![4, 5, 6]; let l2: f64 = Euclidian::new().distance(&a, &b); assert!((l2 - 5.19615242).abs() < 1e-8); } }