//! # Manhattan Distance //! //! The Manhattan distance between two points \\(x \in ℝ^n \\) and \\( y \in ℝ^n \\) in n-dimensional space is the sum of the distances in each dimension. //! //! \\[ d(x, y) = \sum_{i=0}^n \lvert x_i - y_i \rvert \\] //! //! Example: //! //! ``` //! use smartcore::metrics::distance::Distance; //! use smartcore::metrics::distance::manhattan::Manhattan; //! //! let x = vec![1., 1.]; //! let y = vec![2., 2.]; //! //! let l1: f64 = Manhattan::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; /// Manhattan distance #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))] #[derive(Debug, Clone)] pub struct Manhattan { _t: PhantomData, } impl Manhattan { /// instatiate the initial structure pub fn new() -> Manhattan { Manhattan { _t: PhantomData } } } impl Default for Manhattan { fn default() -> Self { Self::new() } } impl> Distance for Manhattan { fn distance(&self, x: &A, y: &A) -> f64 { if x.shape() != y.shape() { panic!("Input vector sizes are different"); } let dist: f64 = x .iterator(0) .zip(y.iterator(0)) .map(|(&a, &b)| (a - b).to_f64().unwrap().abs()) .sum(); dist } } #[cfg(test)] mod tests { use super::*; #[cfg_attr( all(target_arch = "wasm32", not(target_os = "wasi")), wasm_bindgen_test::wasm_bindgen_test )] #[test] fn manhattan_distance() { let a = vec![1., 2., 3.]; let b = vec![4., 5., 6.]; let l1: f64 = Manhattan::new().distance(&a, &b); assert!((l1 - 9.0).abs() < 1e-8); } }