kdtree 
K-dimensional tree in Rust for fast geospatial indexing
##Usage Add kdtree to Cargo.toml
[dependencies] kdtree = "~0.2.0"
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 Core i7:
cargo bench Running target/release/bench-a26a346635ebfc8f running 2 tests test bench_add_to_kdtree_with_1k_3d_points ... bench: 116 ns/iter (+/- 24) test bench_nearest_from_kdtree_with_1k_3d_points ... bench: 2,661 ns/iter (+/- 1,769) test result: ok. 0 passed; 0 failed; 0 ignored; 2 measured
##License MIT
