Struct KDTree 

pub struct KDTree<T: Float + Send + Sync + 'static, D: Distance<T> + 'static> { /* private fields */ }

A KD-Tree for efficient nearest neighbor searches

Type Parameters

• T - The floating point type for coordinates
• D - The distance metric type

Implementations

impl<T: Float + Send + Sync + 'static> KDTree<T, EuclideanDistance<T>>

pub fn new(points: &Array2<T>) -> SpatialResult<Self>

Create a new KD-Tree with default Euclidean distance metric

Arguments

• points - Array of points, each row is a point

Returns

• A new KD-Tree

pub fn with_leaf_size( points: &Array2<T>, leafsize: usize, ) -> SpatialResult<Self>

Create a new KD-Tree with custom leaf size (using Euclidean distance)

Arguments

• points - Array of points, each row is a point
• leafsize - The maximum number of points in a leaf node

Returns

• A new KD-Tree

impl<T: Float + Send + Sync + 'static, D: Distance<T> + 'static> KDTree<T, D>

pub fn with_metric(points: &Array2<T>, metric: D) -> SpatialResult<Self>

Create a new KD-Tree with custom distance metric

Arguments

• points - Array of points, each row is a point
• metric - The distance metric to use

Returns

• A new KD-Tree

pub fn with_options( points: &Array2<T>, metric: D, leafsize: usize, ) -> SpatialResult<Self>

Create a new KD-Tree with custom distance metric and leaf size

Arguments

• points - Array of points, each row is a point
• metric - The distance metric to use
• leafsize - The maximum number of points in a leaf node

Returns

• A new KD-Tree

pub fn query( &self, point: &[T], k: usize, ) -> SpatialResult<(Vec<usize>, Vec<T>)>

Find the k nearest neighbors to a query point

Arguments

• point - Query point
• k - Number of nearest neighbors to find

Returns

• (indices, distances) of the k nearest neighbors

Examples

use scirs2_spatial::KDTree;
use scirs2_core::ndarray::array;

// Create points for the KDTree - use the exact same points from test_kdtree_with_custom_leaf_size
let points = array![[2.0, 3.0], [5.0, 4.0], [9.0, 6.0], [4.0, 7.0], [8.0, 1.0], [7.0, 2.0]];
let kdtree = KDTree::new(&points).unwrap();

// Find the 2 nearest neighbors to [0.5, 0.5]
let (indices, distances) = kdtree.query(&[0.5, 0.5], 2).unwrap();
assert_eq!(indices.len(), 2);
assert_eq!(distances.len(), 2);

pub fn query_radius( &self, point: &[T], radius: T, ) -> SpatialResult<(Vec<usize>, Vec<T>)>

Find all points within a radius of a query point

Arguments

• point - Query point
• radius - Search radius

Returns

• (indices, distances) of points within the radius

Examples

use scirs2_spatial::KDTree;
use scirs2_core::ndarray::array;

let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];
let kdtree = KDTree::new(&points)?;

// Find all points within radius 0.7 of [0.5, 0.5]
let (indices, distances) = kdtree.query_radius(&[0.5, 0.5], 0.7)?;
assert_eq!(indices.len(), 4); // All points are within 0.7 units of [0.5, 0.5]

pub fn count_neighbors(&self, point: &[T], radius: T) -> SpatialResult<usize>

Count the number of points within a radius of a query point

This method is more efficient than query_radius when only the count is needed.

Arguments

• point - Query point
• radius - Search radius

Returns

• Number of points within the radius

Examples

use scirs2_spatial::KDTree;
use scirs2_core::ndarray::array;

let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];
let kdtree = KDTree::new(&points)?;

// Count points within radius 0.7 of [0.5, 0.5]
let count = kdtree.count_neighbors(&[0.5, 0.5], 0.7)?;
assert_eq!(count, 4); // All points are within 0.7 units of [0.5, 0.5]

pub fn shape(&self) -> (usize, usize)

Get the shape of the KD-Tree’s point set

Returns

• A tuple of (n_points, n_dimensions)

pub fn npoints(&self) -> usize

Get the number of points in the KD-Tree

Returns

• The number of points

pub fn ndim(&self) -> usize

Get the dimensionality of the points in the KD-Tree

Returns

• The dimensionality of the points

pub fn leafsize(&self) -> usize

Get the leaf size of the KD-Tree

Returns

• The leaf size

pub fn bounds(&self) -> &Rectangle<T>

Get the bounds of the KD-Tree

Returns

• The bounding rectangle of the entire dataset

Trait Implementations

impl<T: Clone + Float + Send + Sync + 'static, D: Clone + Distance<T> + 'static> Clone for KDTree<T, D>

fn clone(&self) -> KDTree<T, D>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T: Debug + Float + Send + Sync + 'static, D: Debug + Distance<T> + 'static> Debug for KDTree<T, D>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<T: Float + Send + Sync + 'static, D: Distance<T> + 'static> KDTreeOptimized<T, D> for KDTree<T, D>

fn directed_hausdorff_distance( &self, points: &ArrayView2<'_, T>, _seed: Option<u64>, ) -> SpatialResult<(T, usize, usize)>

Compute the directed Hausdorff distance from one point set to another using KD-tree acceleration

fn hausdorff_distance( &self, points: &ArrayView2<'_, T>, seed: Option<u64>, ) -> SpatialResult<T>

Compute the Hausdorff distance between two point sets using KD-tree acceleration

fn batch_nearest_neighbor( &self, points: &ArrayView2<'_, T>, ) -> SpatialResult<(Array1<usize>, Array1<T>)>

Compute the approximate nearest neighbor for each point in a set