10 releases (5 breaking)
0.6.0 | Apr 20, 2024 |
---|---|
0.5.3 | Jan 7, 2024 |
0.5.1 | Jan 9, 2023 |
0.5.0 | Nov 20, 2022 |
0.2.0 | Nov 29, 2020 |
#85 in Algorithms
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kd-tree
k-dimensional tree in Rust.
Fast, simple, and easy to use.
Usage
// construct kd-tree
let kdtree = kd_tree::KdTree::build_by_ordered_float(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);
assert_eq!(found[0].item, &[2.0, 3.0, 1.0]);
assert_eq!(found[1].item, &[1.0, 2.0, 3.0]);
// search points within a sphere
let found = kdtree.within_radius(&[2.0, 1.5, 2.5], 1.5);
assert_eq!(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
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<Item> = KdTree::build_by_ordered_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
.
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:
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:
kd-tree = { version = "...", features = ["nalgebra"] }
Then, you can use it as follows:
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
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>
doesn't own its buffer, butKdTreeN<T, N>
.KdSliceN<T, N>
is notSized
, so it must be dealed in reference mannar.KdSliceN<T, N>
implementsDeref
to[T]
.KdTreeN<T, N>
implementsDeref
toKdSliceN<T, N>
.- Unlike
PathBuf
orString
, which are mutable,KdTreeN<T, N>
is immutable.
&KdSliceN<T, N>
can be constructed directly, not via KdTreeN
, as below:
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.
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.
Features
"serde" feature
[dependencies]
kd-tree = { version = "...", features = ["serde"] }
You can serialize/deserialize KdTree<{serializable type}>
with this feature.
let src: KdTree3<[i32; 3]> = KdTree::build(vec![[1, 2, 3], [4, 5, 6]]);
let json = serde_json::to_string(&src).unwrap();
assert_eq!(json, "[[1,2,3],[4,5,6]]");
let dst: KdTree3<[i32; 3]> = serde_json::from_str(&json).unwrap();
assert_eq!(src, dst);
"nalgebra" feature
[dependencies]
kd-tree = { version = "...", features = ["nalgebra"] }
see above
"nalgebra-serde" feature
[dependencies]
kd-tree = { version = "...", features = ["nalgebra-serde"] }
You can serialize/deserialize KdTree<{nalgebra type}>
with this feature.
use ::nalgebra as na;
let src: KdTree<na::Point3<f64>> = KdTree::build_by_ordered_float(vec![
na::Point3::new(1.0, 2.0, 3.0),
na::Point3::new(4.0, 5.0, 6.0),
]);
let json = serde_json::to_string(&src).unwrap();
assert_eq!(json, "[[1.0,2.0,3.0],[4.0,5.0,6.0]]");
let dst: KdTree3<na::Point3<f64>> = serde_json::from_str(&json).unwrap();
assert_eq!(src, dst);
"rayon" feature
[dependencies]
kd-tree = { version = "...", features = ["rayon"] }
You can build a kd-tree faster with rayon
.
let kdtree = KdTree::par_build_by_ordered_float(vec![...]);
License
This library is distributed under the MIT License.
Dependencies
~0.3–1.4MB
~29K SLoC