K-dimensional tree in Rust for fast geospatial indexing
Add kdtree to Cargo.toml
toml
[dependencies]
kdtree = "~0.2.0"
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)] ); ```
cargo bench with 2.3 GHz Intel Core i7:
```
cargo bench
Running target/release/bench-a26a346635ebfc8f
running 2 tests test benchaddtokdtreewith1k3dpoints ... bench: 116 ns/iter (+/- 24) test benchnearestfromkdtreewith1k3dpoints ... bench: 2,661 ns/iter (+/- 1,769)
test result: ok. 0 passed; 0 failed; 0 ignored; 2 measured ```
MIT