mirror of
https://github.com/andreytkachenko/kdtree-rust.git
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93 lines
4.5 KiB
Markdown
93 lines
4.5 KiB
Markdown
# kdtree-rust [](https://travis-ci.org/fulara/kdtree-rust) [](https://crates.io/crates/fux_kdtree)
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kdtree implementation for rust.
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Implementation uses sliding midpoint variation of the tree. [More Info here](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.74.210&rep=rep1&type=pdf)
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Implementation uses single `Vec<Node>` to store all its contents, allowing for quick access, and no memory fragmentation.
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###Usage
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Tree can only be used with types implementing trait:
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```
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pub trait KdtreePointTrait : Copy {
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fn dims(&self) -> &[f64];
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}
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```
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Thanks to this trait you can use any dimension. Keep in mind that the tree currently only supports up to 3D [#2](/../../issues/2).
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Examplary implementation would be:
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```
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pub struct Point3WithId {
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dims: [f64; 3],
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pub id: i32,
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}
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impl KdtreePointTrait for Point3WithId {
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#[inline] // the inline on this method is important! as without it there is ~25% speed loss on the tree when cross-crate usage.
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fn dims(&self) -> &[f64] {
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return &self.dims;
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}
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}
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```
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Where id is just a example of the way in which I carry the data.
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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, It does support adding the nodes with method 'insert_node' and there is indeed a code to rebuild the tree if depths grows substantially. Basic usage can be found in the integration test, fragment copied below:
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```
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let tree = kdtree::kdtree::Kdtree::new(&mut points.clone());
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//test points pushed into the tree, id should be equal.
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for i in 0 .. point_count {
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let p = &points[i];
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assert_eq!(p.id, tree.nearest_search(p).id );
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}
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```
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##Benchmark
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`cargo bench` using travis :)
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```
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running 3 tests
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test bench_creating_1000_000_node_tree ... bench: 275,155,622 ns/iter (+/- 32,713,321)
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test bench_adding_same_node_to_1000_tree ... bench: 42 ns/iter (+/- 11)
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test bench_creating_1000_node_tree ... bench: 120,310 ns/iter (+/- 4,746)
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test bench_single_lookup_times_for_1000_node_tree ... bench: 164 ns/iter (+/- 139)
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test result: ok. 0 passed; 0 failed; 0 ignored; 4 measured
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```
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~275ms to create a 1000_000 node tree. << this bench is now disabled.
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~120us to create a 1000 node tree.
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160ns to query the tree.
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###Benchmark - comparison with CGAL.
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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
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```
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Benchmark Time CPU Iterations
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-----------------------------------------------------------------
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Cgal_tree_buildup/10 2226 ns 2221 ns 313336
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Cgal_tree_buildup/100 18357 ns 18315 ns 37968
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Cgal_tree_buildup/1000 288135 ns 287345 ns 2369
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Cgal_tree_buildup/9.76562k 3296740 ns 3290815 ns 211
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Cgal_tree_buildup/97.6562k 42909150 ns 42813307 ns 12
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Cgal_tree_buildup/976.562k 734566227 ns 733267760 ns 1
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Cgal_tree_lookup/10 72 ns 72 ns 9392612
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Cgal_tree_lookup/100 95 ns 95 ns 7103628
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Cgal_tree_lookup/1000 174 ns 174 ns 4010773
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Cgal_tree_lookup/9.76562k 268 ns 267 ns 2759487
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Cgal_tree_lookup/97.6562k 881 ns 876 ns 1262454
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Cgal_tree_lookup/976.562k 993 ns 991 ns 713751
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Rust_tree_buildup/10 726 ns 724 ns 856791
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Rust_tree_buildup/100 7103 ns 7092 ns 96132
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Rust_tree_buildup/1000 84879 ns 84720 ns 7927
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Rust_tree_buildup/9.76562k 1012983 ns 1010856 ns 630
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Rust_tree_buildup/97.6562k 12406293 ns 12382399 ns 51
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Rust_tree_buildup/976.562k 197175067 ns 196763387 ns 3
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Rust_tree_lookup/10 62 ns 62 ns 11541505
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Rust_tree_lookup/100 139 ns 139 ns 4058837
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Rust_tree_lookup/1000 220 ns 220 ns 2890813
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Rust_tree_lookup/9.76562k 307 ns 307 ns 2508133
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Rust_tree_lookup/97.6562k 362 ns 362 ns 2035671
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Rust_tree_lookup/976.562k 442 ns 441 ns 1636130
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```
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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.
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##License
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The Unlicense
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