K-dimensional tree for Rust(bucket point-region implementation)
use kdtree::KdTree;
use kdtree::ErrorKind;
use kdtree::distance::square_euclidean;
let a: ([f64; 2], usize) = ([0f64, 0f64], 0);
let b: ([f64; 2], usize) = ([1f64, 1f64], 1);
let c: ([f64; 2], usize) = ([2f64, 2f64], 2);
let d: ([f64; 2], usize) = ([3f64, 3f64], 3);
let dimensions = 2;
let mut kd_tree = KdTree::new(dimensions);
kd_tree.add(&a.0, a.1).unwrap();
kd_tree.add(&b.0, b.1).unwrap();
kd_tree.add(&c.0, c.1).unwrap();
kd_tree.add(&d.0, d.1).unwrap();
assert_eq!(kd_tree.size(), 4);
assert_eq!(
kd_tree.nearest(&a.0, 0, &square_euclidean).unwrap(),
vec![]
);
assert_eq!(
kd_tree.nearest(&a.0, 1, &square_euclidean).unwrap(),
vec![(0f64, &0)]
);
assert_eq!(
kd_tree.nearest(&a.0, 2, &square_euclidean).unwrap(),
vec![(0f64, &0), (2f64, &1)]
);
assert_eq!(
kd_tree.nearest(&a.0, 3, &square_euclidean).unwrap(),
vec![(0f64, &0), (2f64, &1), (8f64, &2)]
);
assert_eq!(
kd_tree.nearest(&a.0, 4, &square_euclidean).unwrap(),
vec![(0f64, &0), (2f64, &1), (8f64, &2), (18f64, &3)]
);
assert_eq!(
kd_tree.nearest(&a.0, 5, &square_euclidean).unwrap(),
vec![(0f64, &0), (2f64, &1), (8f64, &2), (18f64, &3)]
);
assert_eq!(
kd_tree.nearest(&b.0, 4, &square_euclidean).unwrap(),
vec![(0f64, &1), (2f64, &0), (2f64, &2), (8f64, &3)]
);