Crate kiddo

kiddo

K-dimensional tree for Rust (bucket point-region implementation). A fork of kdtree. Refactored to use const generics, with some performance improvements and extra features. Thanks and kudos to mrhooray for the original kdtree library on which kiddo is based.

Ideal for neareast-neighbour stype queries on astronomical and geospatial datasets.

Usage

use kiddo::KdTree;
use kiddo::ErrorKind;
use kiddo::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 mut kdtree = KdTree::new();

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

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

Re-exports

pub use crate::kiddo::ErrorKind;

pub use crate::kiddo::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.
kiddo