Crate kdtree_simd

kdtree

K-dimensional tree for Rust (bucket point-region implementation)

Usage

use kdtree::KdTree;
use kdtree::ErrorKind;
use kdtree::distance::squared_euclidean;

let a: ([f64; 2], usize) = ([0f64, 0f64], 0);
let b: ([f64; 2], usize) = ([1f64, 1f64], 1);
let c: ([f64; 2], usize) = ([2f64, 2f64], 2);
let d: ([f64; 2], usize) = ([3f64, 3f64], 3);

let dimensions = 2;
let mut kdtree = KdTree::new(dimensions);

kdtree.add(&a.0, a.1).unwrap();
kdtree.add(&b.0, b.1).unwrap();
kdtree.add(&c.0, c.1).unwrap();
kdtree.add(&d.0, d.1).unwrap();

assert_eq!(kdtree.size(), 4);
assert_eq!(
    kdtree.nearest(&a.0, 0, &squared_euclidean).unwrap(),
    vec![]
);
assert_eq!(
    kdtree.nearest(&a.0, 1, &squared_euclidean).unwrap(),
    vec![(0f64, &0)]
);
assert_eq!(
    kdtree.nearest(&a.0, 2, &squared_euclidean).unwrap(),
    vec![(0f64, &0), (2f64, &1)]
);
assert_eq!(
    kdtree.nearest(&a.0, 3, &squared_euclidean).unwrap(),
    vec![(0f64, &0), (2f64, &1), (8f64, &2)]
);
assert_eq!(
    kdtree.nearest(&a.0, 4, &squared_euclidean).unwrap(),
    vec![(0f64, &0), (2f64, &1), (8f64, &2), (18f64, &3)]
);
assert_eq!(
    kdtree.nearest(&a.0, 5, &squared_euclidean).unwrap(),
    vec![(0f64, &0), (2f64, &1), (8f64, &2), (18f64, &3)]
);
assert_eq!(
    kdtree.nearest(&b.0, 4, &squared_euclidean).unwrap(),
    vec![(0f64, &1), (2f64, &0), (2f64, &2), (8f64, &3)]
);

Re-exports

pub use crate::kdtree::ErrorKind;

pub use crate::kdtree::KdTree;

Modules

distance

Defines different distance metrics, in simplest case it defines the euclidean distance which is no more than the square root of the sum of the squares of the distances in each dimension.

kdtree

simd_euclidean