kdtree-rust

kdtree implementation for rust.

Implementation uses sliding midpoint variation of the tree. More Info here

###Usage Tree can only be used with types implementing trait:

pub trait KdtreePointTrait : Copy  {
    fn dims(&self) -> &[f64];
}

Thanks to this trait you can use any dimension. Keep in mind that the tree currently only supports up to 3D.
Examplary implementation would be:

pub struct Point3WithId {
    dims: [f64; 3],
    pub id: i32,
}

impl KdtreePointTrait for Point3WithId {
    fn dims(&self) -> &[f64] {
        return &self.dims;
    }
}

Where id is just a example of the way in which I carry the data.
With that trait implemented you are good to go to use the tree. Keep in mind that the kdtree is not a self balancing tree, so it should not support continous add. right now the tree just handles the build up from Vec. Basic usage can be found in the integration test, fragment copied below:

let tree = kdtree::kdtree::Kdtree::new(&mut points.clone());

//test points pushed into the tree, id should be equal.
for i in 0 .. point_count {
    let p = &points[i];

    assert_eq!(p.id, tree.nearest_search(p).id );
}

##Benchmark cargo bench using travis :)

running 3 tests
test bench_creating_1000_000_node_tree          ... bench: 275,155,622 ns/iter (+/- 32,713,321)
test bench_creating_1000_node_tree              ... bench:     121,314 ns/iter (+/- 1,977)
test bench_single_loop_times_for_1000_node_tree ... bench:         162 ns/iter (+/- 76)
test result: ok. 0 passed; 0 failed; 0 ignored; 3 measured

~275ms to create a 1000_000 node tree. << this bench is now disabled.
~120us to create a 1000 node tree.
160ns to query the tree.

###Benchmark - comparison with CGAL. Since raw values arent saying much I've created the benchmark comparing this implementation against CGAL. code of the benchmark is available here: https://github.com/fulara/kdtree-benchmarks

Benchmark                           Time           CPU Iterations
-----------------------------------------------------------------
Cgal_tree_buildup/10             2226 ns       2221 ns     313336
Cgal_tree_buildup/100           18357 ns      18315 ns      37968
Cgal_tree_buildup/1000         288135 ns     287345 ns       2369
Cgal_tree_buildup/9.76562k    3296740 ns    3290815 ns        211
Cgal_tree_buildup/97.6562k   42909150 ns   42813307 ns         12
Cgal_tree_buildup/976.562k  734566227 ns  733267760 ns          1
Cgal_tree_lookup/10                72 ns         72 ns    9392612
Cgal_tree_lookup/100               95 ns         95 ns    7103628
Cgal_tree_lookup/1000             174 ns        174 ns    4010773
Cgal_tree_lookup/9.76562k         268 ns        267 ns    2759487
Cgal_tree_lookup/97.6562k         881 ns        876 ns    1262454
Cgal_tree_lookup/976.562k         993 ns        991 ns     713751
Rust_tree_buildup/10              726 ns        724 ns     856791
Rust_tree_buildup/100            7103 ns       7092 ns      96132
Rust_tree_buildup/1000          84879 ns      84720 ns       7927
Rust_tree_buildup/9.76562k    1012983 ns    1010856 ns        630
Rust_tree_buildup/97.6562k   12406293 ns   12382399 ns         51
Rust_tree_buildup/976.562k  197175067 ns  196763387 ns          3
Rust_tree_lookup/10                62 ns         62 ns   11541505
Rust_tree_lookup/100              139 ns        139 ns    4058837
Rust_tree_lookup/1000             220 ns        220 ns    2890813
Rust_tree_lookup/9.76562k         307 ns        307 ns    2508133
Rust_tree_lookup/97.6562k         362 ns        362 ns    2035671
Rust_tree_lookup/976.562k         442 ns        441 ns    1636130

Rust_tree_lookup has some overhead since the libraries are being invoked from C code into Rust, and there is minor overhead of that in between, my experience indicates around 50 ns overhead.

##License The Unlicense

fux_kdtree 0.1.0

kdtree-rust