https://github.com/travisstaloch/kd-tree
A k-d tree implementation in zig
https://github.com/travisstaloch/kd-tree
zig-library zig-package
Last synced: 7 months ago
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A k-d tree implementation in zig
- Host: GitHub
- URL: https://github.com/travisstaloch/kd-tree
- Owner: travisstaloch
- License: mit
- Created: 2024-08-10T19:39:04.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-02T22:21:01.000Z (over 1 year ago)
- Last Synced: 2025-04-10T05:46:14.508Z (9 months ago)
- Topics: zig-library, zig-package
- Language: Zig
- Homepage:
- Size: 24.4 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# About
A [k-d tree](https://en.wikipedia.org/wiki/K-d_tree) implementation ported from https://github.com/gishi523/kd-tree
# API
* Floats only. Accepts arrays or homogenous structures of any zig float type
* Search for single nearest neighbor
* Search for k nearest neighbors
* Search for all neighbors within radius
* Visit all nodes with user provided context
* Validate all nodes
# Usage
This package is meant to be consumed with the [zig build system](https://ziglang.org/learn/build-system) and imported as a module.
```console
# latest commit
zig fetch --save=kdtree git+https://github.com/travisstaloch/kd-tree
```
```console
# specific commit
zig fetch --save=kdtree https://github.com/travisstaloch/kd-tree/archive/.tar.gz
```
```zig
// build.zig
exe.root_module.addImport("kdtree", b.dependency("kdtree", .{}).module("kdtree"));
```
```zig
// myapp.zig
const kdtree = @import("kdtree");
```
### Example test
This code is from [src/root.zig](src/root.zig) where there are also other similar tests. And there is [demo raylib app](src/main.zig) too.
```zig
test "basic usage" {
const MyPoint = struct { x: f32, y: f32 };
const my_points: [8]MyPoint = .{
.{ .x = 2.7175806e0, .y = 2.5162528e0 },
.{ .x = 3.0172718e0, .y = 1.1619115e-1 },
.{ .x = 3.9525537e0, .y = 2.9364395e-1 },
.{ .x = 7.0706835e0, .y = 7.9604106e0 },
.{ .x = 2.6941679e0, .y = 1.9487169 },
.{ .x = 3.7812352e0, .y = 8.3156556e-1 },
.{ .x = 7.326651e-1, .y = 1.2251115e0 },
.{ .x = 8.401675e-1, .y = 2.9021428e0 },
};
// TODO: const KdTree = @import("kdtree").KdTree;
const Tree = KdTree(MyPoint, .{});
var tree = try Tree.init(std.testing.allocator, &my_points);
defer tree.deinit(std.testing.allocator);
// validate
try tree.validate();
// visit
var count: u8 = 0;
tree.visit(.{}, &count, struct {
fn visit(_: Tree, _: Tree.Node, c: *u8) bool {
c.* += 1;
return true;
}
}.visit);
try testing.expectEqual(my_points.len, count);
const target: MyPoint = .{ .x = 5, .y = 5 };
// nearest
const nearest = tree.nnSearch(target);
try testing.expectEqual(0, nearest.nearest_point_idx);
// knn
var knn_result: [2]u32 = undefined;
try tree.knnSearch(std.testing.allocator, target, &knn_result);
try testing.expectEqual(0, knn_result[0]);
try testing.expectEqual(3, knn_result[1]);
// radius
var radius_search_results = std.ArrayList(u32).init(std.testing.allocator);
defer radius_search_results.deinit();
const radius = 4;
try tree.radiusSearch(target, radius, &radius_search_results);
try testing.expectEqual(3, radius_search_results.items.len);
}
```
# Demo
```console
zig build run
```
red is the target point and radius. green + outlined is the closest neighbor. green are the 5 nearest neighbors. and blue are within the search radius.
If you want to run the demo app, you'll need to either have raylib available on your system or create a deps/raylib folder and put libraylib.a there along with include/{raylib.h,raymath.h,rlgl.h}
# Bench
```console
$ ./bench.sh unstable
...
++ zig build -Doptimize=ReleaseFast
++ zig-out/bin/bench unstable
init time 56.034ms
validate time 215.078us
nnSearch time 9.28us
knnSearch time 838.642us
radiusSearch time 4.85us
time 57.105ms size 100000
$ ./bench.sh median_of_medians
...
++ zig build -Doptimize=ReleaseFast
++ zig-out/bin/bench
init time 15.637ms
validate time 250.359us
nnSearch time 9.29us
knnSearch time 861.343us
radiusSearch time 5.34us
time 16.767ms size 100000
```