kd-tree

k-dimensional tree in Rust.

Fast, simple, and easy to use.

Usage

```rust // construct kd-tree let kdtree = kdtree::KdTree::buildbyorderedfloat(vec![ [1.0, 2.0, 3.0], [3.0, 1.0, 2.0], [2.0, 3.0, 1.0], ]);

// search the nearest neighbor let found = kdtree.nearest(&[3.1, 0.9, 2.1]).unwrap(); assert_eq!(found.item, &[3.0, 1.0, 2.0]);

// search k-nearest neighbors let found = kdtree.nearests(&[1.5, 2.5, 1.8], 2); asserteq!(found[0].item, &[2.0, 3.0, 1.0]); asserteq!(found[1].item, &[1.0, 2.0, 3.0]);

// search points within a sphere let found = kdtree.withinradius(&[2.0, 1.5, 2.5], 1.5); asserteq!(found.len(), 2); assert!(found.iter().any(|&&p| p == [1.0, 2.0, 3.0])); assert!(found.iter().any(|&&p| p == [3.0, 1.0, 2.0])); ```

With or without KdPoint

KdPoint trait represents k-dimensional point.

You can live with or without KdPoint.

With KdPoint explicitly

```rust use kd_tree::{KdPoint, KdTree};

// define your own item type. struct Item { point: [f64; 2], id: usize, }

// implement KdPoint for your item type. impl KdPoint for Item { type Scalar = f64; type Dim = typenum::U2; // 2 dimensional tree. fn at(&self, k: usize) -> f64 { self.point[k] } }

// construct kd-tree from Vec<Item>. // Note: you need to use build_by_ordered_float() because f64 doesn't implement Ord trait. let kdtree: KdTree = KdTree::buildbyordered_float(vec![ Item { point: [1.0, 2.0], id: 111 }, Item { point: [2.0, 3.0], id: 222 }, Item { point: [3.0, 4.0], id: 333 }, ]);

// search nearest item from [1.9, 3.1] assert_eq!(kdtree.nearest(&[1.9, 3.1]).unwrap().item.id, 222); ```

With KdPoint implicitly

KdPoint trait is implemented for fixed-sized array of numerical types, such as [f64; 3] or [i32, 2] etc. So you can build kd-trees of those types without custom implementation of KdPoint.

rust let items: Vec<[i32; 3]> = vec![[1, 2, 3], [3, 1, 2], [2, 3, 1]]; let kdtree = kd_tree::KdTree::build(items); assert_eq!(kdtree.nearest(&[3, 1, 2]).unwrap().item, &[3, 1, 2]);

KdPoint trait is also implemented for tuple of a KdPoint and an arbitrary type, like (P, T) where P: KdPoint. And a type alias named KdMap<P, T> is defined as KdTree<(P, T)>. So you can build a kd-tree from key-value pairs, as below:

rust let kdmap: kd_tree::KdMap<[isize; 3], &'static str> = kd_tree::KdMap::build(vec![ ([1, 2, 3], "foo"), ([2, 3, 1], "bar"), ([3, 1, 2], "buzz"), ]); assert_eq!(kdmap.nearest(&[3, 1, 2]).unwrap().item.1, "buzz");

nalgebra feature

KdPoint trait is implemented for nalgebra's vectors and points.

Enable nalgebra feature in your Cargo.toml: toml kd-tree = { version = "...", features = ["nalgebra"] } Then, you can use it as follows: rust use nalgebra::Point3; let items: Vec<Point3<i32>> = vec![ Point3::new(1, 2, 3), Point3::new(3, 1, 2), Point3::new(2, 3, 1) ]; let kdtree = kd_tree::KdTree::build(items); let query = Point3::new(3, 1, 2); assert_eq!(kdtree.nearest(&query).unwrap().item, &query);

Without KdPoint

rust use std::collections::HashMap; let items: HashMap<&'static str, [i32; 2]> = vec![ ("a", [10, 20]), ("b", [20, 10]), ("c", [20, 20]), ].into_iter().collect(); let kdtree = kd_tree::KdTree2::build_by_key(items.keys().collect(), |key, k| items[*key][k]); assert_eq!(kdtree.nearest_by(&[18, 21], |key, k| items[*key][k]).unwrap().item, &&"c");

To own, or not to own

KdSliceN<T, N> and KdTreeN<T, N> are similar to str and String, or Path and PathBuf.

&KdSliceN<T, N> can be constructed directly, not via KdTreeN, as below:

rust let mut items: Vec<[i32; 3]> = vec![[1, 2, 3], [3, 1, 2], [2, 3, 1]]; let kdtree = kd_tree::KdSlice::sort(&mut items); assert_eq!(kdtree.nearest(&[3, 1, 2]).unwrap().item, &[3, 1, 2]);

KdIndexTreeN

A KdIndexTreeN refers a slice of items, [T], and contains kd-tree of indices to the items, KdTreeN<usize, N>. Unlike [KdSlice::sort], [KdIndexTree::build] doesn't sort input items.

rust let items = vec![[1, 2, 3], [3, 1, 2], [2, 3, 1]]; let kdtree = kd_tree::KdIndexTree::build(&items); assert_eq!(kdtree.nearest(&[3, 1, 2]).unwrap().item, &1); // nearest() returns an index of found item.