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kdtree rust crates.io docs license

K-dimensional tree in Rust for fast geospatial indexing and nearest neighbors lookup

Usage

Add kdtree to Cargo.toml

[dependencies]
kdtree = "0.7.0"

Add points to kdtree and query nearest n points with distance function

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)]
);

Benchmark

cargo bench with 2.3 GHz Intel i5-7360U:

cargo bench
     Running target/release/deps/bench-9e622e6a4ed9b92a

running 2 tests
test bench_add_to_kdtree_with_1k_3d_points       ... bench:         106 ns/iter (+/- 25)
test bench_nearest_from_kdtree_with_1k_3d_points ... 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

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.