andrey/kdtree-rust
1
0
Fork 0
mirror of https://github.com/andreytkachenko/kdtree-rust.git synced 2024-11-22 09:26:25 +04:00
Code Issues Projects Releases Wiki Activity

Added bench, improvements in nn code

Browse Source
This commit is contained in:
Olek 2016-12-26 21:22:10 +01:00
parent 9ac0a899e2
commit b9fa192758
2 changed files with 46 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

39
src/bench.rs
View File

@ -27,6 +27,27 @@ impl kdtree::kdtree::KdtreePointTrait for Point2WithId {
}
}
#[derive(Copy, Clone, PartialEq)]
pub struct Point3WithId {
dims: [f64; 3],
pub id: i32,
}
impl Point3WithId {
pub fn new(id: i32, x: f64, y: f64, z: f64) -> Point3WithId {
Point3WithId {
dims: [x, y, z],
id: id,
}
}
}
impl kdtree::kdtree::KdtreePointTrait for Point3WithId {
fn dims(&self) -> &[f64] {
return &self.dims;
}
}
fn a(b: &mut Bencher) {
let len = 1000usize;
let mut points : Vec<Point2WithId> = vec![];
@ -42,5 +63,21 @@ fn a(b: &mut Bencher) {
});
}
benchmark_group!(benches, a);
fn b(b: &mut Bencher) {
let len = 1000usize;
let mut points : Vec<Point3WithId> = vec![];
//let mut kdtree = KdTree::new_with_capacity(3, 16);
for i in 0..len {
points.push(Point3WithId::new(i as i32, rand::random(),rand::random(),rand::random()))
}
let tree = kdtree::kdtree::Kdtree::new(points.clone());
b.iter(|| tree.nearest_search(&points[0]));
}
benchmark_group!(benches, a,b);
benchmark_main!(benches);

21
src/kdtree/mod.rs
View File

@ -47,19 +47,15 @@ impl<T: KdtreePointTrait + Copy> Kdtree<T> {
let splitting_value = node.split_on;
let point_splitting_dim_value = p.dims()[dimension];
let mut closer_node : Option<usize>;
let mut farther_node : Option<usize>;
let (closer_node, farther_node) =
if point_splitting_dim_value <= splitting_value {
closer_node = node.left_node;
farther_node = node.right_node;
(node.left_node, node.right_node)
} else {
closer_node = node.right_node;
farther_node = node.left_node;
}
(node.right_node, node.left_node)
};
if closer_node.is_some() {
self.nearest_search_impl(p, closer_node.unwrap(), best_distance_squared, best_leaf_found);
if let Some(closer_node) = closer_node {
self.nearest_search_impl(p, closer_node, best_distance_squared, best_leaf_found);
}
let distance = squared_euclidean(p.dims(), node.point.dims());
@ -68,14 +64,13 @@ impl<T: KdtreePointTrait + Copy> Kdtree<T> {
*best_leaf_found = searched_index;
}
if(farther_node.is_some()) {
if let Some(farther_node) = farther_node {
let distance_on_single_dimension = squared_euclidean(&[splitting_value],&[point_splitting_dim_value]);
if distance_on_single_dimension <= *best_distance_squared {
self.nearest_search_impl(p, farther_node.unwrap(), best_distance_squared, best_leaf_found);
self.nearest_search_impl(p, farther_node, best_distance_squared, best_leaf_found);
}
}
}