kdtree

K-dimensional tree in Rust for fast geospatial indexing

Usage

Add kdtree to Cargo.toml toml [dependencies] kdtree = "0.5.0"

Add points to kdtree and query nearest n points with distance function ```rust 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();

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

Benchmark

cargo bench with 2.3 GHz Intel i5-7360U: ``` cargo bench Running target/release/deps/bench-9e622e6a4ed9b92a

running 2 tests test benchaddtokdtreewith1k3dpoints ... bench: 106 ns/iter (+/- 25) test benchnearestfromkdtreewith1k3dpoints ... bench: 1,237 ns/iter (+/- 266)

test result: ok. 0 passed; 0 failed; 0 ignored; 2 measured; 0 filtered out ``` Thanks Eh2406 for various fixes and perf improvements.

License

Licensed under either of

Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
MIT License (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.