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https://github.com/mrhooray/kdtree-rs
K-dimensional tree in Rust for fast geospatial indexing and lookup
https://github.com/mrhooray/kdtree-rs
geo-spatial index k-dimensional nearest-neighbor-search rust search tree
Last synced: about 1 month ago
JSON representation
K-dimensional tree in Rust for fast geospatial indexing and lookup
- Host: GitHub
- URL: https://github.com/mrhooray/kdtree-rs
- Owner: mrhooray
- License: apache-2.0
- Created: 2015-07-29T05:20:17.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2024-02-02T20:44:49.000Z (4 months ago)
- Last Synced: 2024-04-16T01:00:42.895Z (about 2 months ago)
- Topics: geo-spatial, index, k-dimensional, nearest-neighbor-search, rust, search, tree
- Language: Rust
- Homepage:
- Size: 79.1 KB
- Stars: 213
- Watchers: 7
- Forks: 53
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE-APACHE
Lists
- Awesome-Geospatial - kdtree-rs - K-dimensional tree in Rust for fast geospatial indexing and nearest neighbors lookup. (Rust)
- awesome-rust - mrhooray/kdtree-rs - dimensional tree for fast geospatial indexing and nearest neighbors lookup (Libraries / Data structures)
- awesome-rust - mrhooray/kdtree-rs - dimensional tree in Rust for fast geospatial indexing and nearest neighbors lookup (Libraries / Data structures)
- awesome-stars - kdtree-rs - K-dimensional tree in Rust for fast geospatial indexing and lookup (Rust)
- awesome-stars - mrhooray/kdtree-rs - K-dimensional tree in Rust for fast geospatial indexing and lookup (Rust)
- awesome-rust-cn - mrhooray/kdtree-rs
- awesome-rust - mrhooray/kdtree-rs - dimensional tree in Rust for fast geospatial indexing and nearest neighbors lookup (Libraries / Data structures)
README
# kdtree [](https://github.com/mrhooray/kdtree-rs/actions/workflows/rust.yaml) [](https://crates.io/crates/kdtree) [](https://docs.rs/kdtree) [](https://github.com/mrhooray/kdtree-rs#license)
> K-dimensional tree in Rust for fast geospatial indexing and nearest neighbors lookup
- [Crate](https://crates.io/crates/kdtree)
- [Documentation](https://docs.rs/kdtree)
- [Usage](#usage)
- [Benchmark](#benchmark)
- [License](#license)
## Usage
Add `kdtree` to `Cargo.toml`
```toml
[dependencies]
kdtree = "0.7.0"
```
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();
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](https://github.com/Eh2406) for various fixes and perf improvements.
## License
Licensed under either of
- Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)
- MIT License ([LICENSE-MIT](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.