Struct KdIndexTreeN

pub struct KdIndexTreeN<'a, T, N: Unsigned> { /* private fields */ }

This type refers a slice of items, [T], and contains kd-tree of indices to the items, KdTree<usize, N>. Unlike KdSliceN::sort, KdIndexTreeN::build doesn’t sort input items.

let items = vec![[1, 2, 3], [3, 1, 2], [2, 3, 1]];
let kdtree = kd_tree::KdIndexTree::build(&items);
assert_eq!(kdtree.nearest(&[3, 1, 2]).unwrap().item, &1); // nearest() returns an index of items.

Implementations

impl<'a, T, N: Unsigned> KdIndexTreeN<'a, T, N>

pub fn source(&self) -> &'a [T]

pub fn indices(&self) -> &KdSliceN<usize, N>

pub fn item(&self, i: usize) -> &'a T

pub fn build_by<F>(source: &'a [T], compare: F) -> Self

where F: Fn(&T, &T, usize) -> Ordering + Copy,

pub fn build_by_key<Key, F>(source: &'a [T], kd_key: F) -> Self

where Key: Ord, F: Fn(&T, usize) -> Key + Copy,

pub fn build_by_ordered_float(points: &'a [T]) -> Self

where T: KdPoint<Dim = N>, T::Scalar: FloatCore,

pub fn build(points: &'a [T]) -> Self

where T: KdPoint<Dim = N>, T::Scalar: Ord,

pub fn nearest_by<Q: KdPoint<Dim = N>>( &self, query: &Q, coord: impl Fn(&T, usize) -> Q::Scalar + Copy, ) -> Option<ItemAndDistance<'_, usize, Q::Scalar>>

pub fn nearest( &self, query: &impl KdPoint<Scalar = T::Scalar, Dim = N>, ) -> Option<ItemAndDistance<'_, usize, T::Scalar>>

where T: KdPoint<Dim = N>,

Example

let mut items: Vec<[i32; 3]> = vec![[1, 2, 3], [3, 1, 2], [2, 3, 1]];
let kdtree = kd_tree::KdIndexTree3::build(&items);
assert_eq!(kdtree.nearest(&[3, 1, 2]).unwrap().item, &1);

pub fn nearests_by<Q: KdPoint<Dim = N>>( &self, query: &Q, num: usize, coord: impl Fn(&T, usize) -> Q::Scalar + Copy, ) -> Vec<ItemAndDistance<'_, usize, Q::Scalar>>

pub fn nearests( &self, query: &impl KdPoint<Scalar = T::Scalar, Dim = N>, num: usize, ) -> Vec<ItemAndDistance<'_, usize, T::Scalar>>

where T: KdPoint<Dim = N>,

Returns kNN(k nearest neighbors) from the input point.

Example

let mut items: Vec<[i32; 3]> = vec![[1, 2, 3], [3, 1, 2], [2, 3, 1], [3, 2, 2]];
let kdtree = kd_tree::KdIndexTree::build(&mut items);
let nearests = kdtree.nearests(&[3, 1, 2], 2);
assert_eq!(nearests.len(), 2);
assert_eq!(nearests[0].item, &1);
assert_eq!(nearests[1].item, &3);

pub fn within_by_cmp( &self, compare: impl Fn(&T, usize) -> Ordering + Copy, ) -> Vec<&usize>

pub fn within_by<Q: KdPoint<Dim = N>>( &self, query: &[Q; 2], coord: impl Fn(&T, usize) -> Q::Scalar + Copy, ) -> Vec<&usize>

pub fn within( &self, query: &[impl KdPoint<Scalar = T::Scalar, Dim = N>; 2], ) -> Vec<&usize>

where T: KdPoint<Dim = N>,

pub fn within_radius_by<Q: KdPoint<Dim = N>>( &self, query: &Q, radius: Q::Scalar, coord: impl Fn(&T, usize) -> Q::Scalar + Copy, ) -> Vec<&usize>

pub fn within_radius( &self, query: &impl KdPoint<Scalar = T::Scalar, Dim = N>, radius: T::Scalar, ) -> Vec<&usize>

where T: KdPoint<Dim = N>,

Trait Implementations

impl<'a, T: Clone, N: Clone + Unsigned> Clone for KdIndexTreeN<'a, T, N>

fn clone(&self) -> KdIndexTreeN<'a, T, N>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<'a, T: Debug, N: Debug + Unsigned> Debug for KdIndexTreeN<'a, T, N>

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

Formats the value using the given formatter.

impl<'a, T: PartialEq, N: PartialEq + Unsigned> PartialEq for KdIndexTreeN<'a, T, N>

fn eq(&self, other: &KdIndexTreeN<'a, T, N>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<'a, T: Eq, N: Eq + Unsigned> Eq for KdIndexTreeN<'a, T, N>

impl<'a, T, N: Unsigned> StructuralPartialEq for KdIndexTreeN<'a, T, N>