About

A 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 and imported as a module.

# latest commit
zig fetch --save=kdtree git+https://github.com/travisstaloch/kd-tree

# specific commit
zig fetch --save=kdtree https://github.com/travisstaloch/kd-tree/archive/<commit-hash>.tar.gz

// build.zig
exe.root_module.addImport("kdtree", b.dependency("kdtree", .{}).module("kdtree"));

// myapp.zig
const kdtree = @import("kdtree");

Example test

This code is from src/root.zig where there are also other similar tests. And there is demo raylib app too.

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

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

$ ./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