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ArchivedKdTree

kiddo::float::kdtree

Struct ArchivedKdTree 

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pub struct ArchivedKdTree<A: Copy + Default, T, const K: usize, const B: usize, IDX>
where
    Vec<LeafNode<A, T, K, B, IDX>>: Archive,
    Vec<StemNode<A, K, IDX>>: Archive,
    IDX: Archive,
    T: Archive + Copy + Default,
{ /* private fields */ }
Expand description

An archived KdTree

Implementations§

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impl<A: Axis + Archive<Archived = A>, T: Content + Archive<Archived = T>, const K: usize, const B: usize, IDX: Index<T = IDX> + Archive<Archived = IDX>> ArchivedKdTree<A, T, K, B, IDX>
where usize: Cast<IDX>,

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pub fn size(&self) -> T

Returns the current number of elements stored in the tree

§Examples
use kiddo::KdTree;

let mut tree: KdTree<f64, 3> = KdTree::new();

tree.add(&[1.0, 2.0, 5.0], 100);
tree.add(&[1.1, 2.1, 5.1], 101);

assert_eq!(tree.size(), 2);
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impl<A: Axis + Archive<Archived = A>, T: Content + Archive<Archived = T>, const K: usize, const B: usize, IDX: Index<T = IDX> + Archive<Archived = IDX>> ArchivedKdTree<A, T, K, B, IDX>
where usize: Cast<IDX>,

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pub fn best_n_within<D>( &self, query: &[A; K], dist: A, max_qty: usize, ) -> impl Iterator<Item = BestNeighbour<A, T>>
where D: DistanceMetric<A, K>,

Finds the “best” n elements within dist of query.

Results are returned in arbitrary order. ‘Best’ is determined by performing a comparison of the elements using < (ie, std::cmp::Ordering::is_lt). Returns an iterator. Returns an iterator.

§Examples
    use kiddo::KdTree;
    use kiddo::best_neighbour::BestNeighbour;
    use kiddo::SquaredEuclidean;

    use std::fs::File;
    use memmap::MmapOptions;

    let mmap = unsafe { MmapOptions::new().map(&File::open("./examples/float-doctest-tree.rkyv").expect("./examples/float-doctest-tree.rkyv missing")).unwrap() };
    let tree = unsafe { rkyv::archived_root::<KdTree<f64, 3>>(&mmap) };

    let mut best_n_within = tree.best_n_within::<SquaredEuclidean>(&[1.0, 2.0, 5.0], 10f64, 1);
    let first = best_n_within.next().unwrap();

    assert_eq!(first, BestNeighbour { distance: 0.0, item: 100 });
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impl<A: Axis + Archive<Archived = A>, T: Content + Archive<Archived = T>, const K: usize, const B: usize, IDX: Index<T = IDX> + Archive<Archived = IDX>> ArchivedKdTree<A, T, K, B, IDX>
where usize: Cast<IDX>,

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pub fn nearest_n<D>( &self, query: &[A; K], qty: usize, ) -> Vec<NearestNeighbour<A, T>>
where D: DistanceMetric<A, K>,

Finds the nearest qty elements to query, using the specified distance metric function.

§Examples
    use kiddo::KdTree;
    use kiddo::SquaredEuclidean;

    use std::fs::File;
    use memmap::MmapOptions;

    let mmap = unsafe { MmapOptions::new().map(&File::open("./examples/float-doctest-tree.rkyv").expect("./examples/float-doctest-tree.rkyv missing")).unwrap() };
    let tree = unsafe { rkyv::archived_root::<KdTree<f64, 3>>(&mmap) };

    let nearest: Vec<_> = tree.nearest_n::<SquaredEuclidean>(&[1.0, 2.0, 5.1], 1);

    assert_eq!(nearest.len(), 1);
    assert!((nearest[0].distance - 0.01f64).abs() < f64::EPSILON);
    assert_eq!(nearest[0].item, 100);
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impl<A: Axis + Archive<Archived = A>, T: Content + Archive<Archived = T>, const K: usize, const B: usize, IDX: Index<T = IDX> + Archive<Archived = IDX>> ArchivedKdTree<A, T, K, B, IDX>
where usize: Cast<IDX>,

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pub fn nearest_n_within<D>( &self, query: &[A; K], dist: A, max_items: NonZero<usize>, sorted: bool, ) -> Vec<NearestNeighbour<A, T>>
where D: DistanceMetric<A, K>,

Finds up to n elements within dist of query, using the specified distance metric function.

Results are returned in as a ResultCollection, which can return a sorted or unsorted Vec.

§Examples
use std::num::NonZero;
use kiddo::KdTree;
use kiddo::SquaredEuclidean;
use std::fs::File;
use memmap::MmapOptions;

let mmap = unsafe { MmapOptions::new().map(&File::open("./examples/float-doctest-tree.rkyv").expect("./examples/float-doctest-tree.rkyv missing")).unwrap() };
let tree = unsafe { rkyv::archived_root::<KdTree<f64, 3>>(&mmap) };
let max_qty = NonZero::new(1).unwrap();
let within = tree.nearest_n_within::<SquaredEuclidean>(&[1.0, 2.0, 5.0], 10f64, max_qty, true);

assert_eq!(within.len(), 1);
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pub fn nearest_n_within_exclusive<D>( &self, query: &[A; K], dist: A, max_items: NonZero<usize>, sorted: bool, inclusive: bool, ) -> Vec<NearestNeighbour<A, T>>
where D: DistanceMetric<A, K>,

Finds up to n elements within dist of query, using the specified distance metric function.

Results are returned in as a ResultCollection, which can return a sorted or unsorted Vec.

§Examples
use std::num::NonZero;
use kiddo::KdTree;
use kiddo::SquaredEuclidean;
use std::fs::File;
use memmap::MmapOptions;

let mmap = unsafe { MmapOptions::new().map(&File::open("./examples/float-doctest-tree.rkyv").expect("./examples/float-doctest-tree.rkyv missing")).unwrap() };
let tree = unsafe { rkyv::archived_root::<KdTree<f64, 3>>(&mmap) };
let max_qty = NonZero::new(1).unwrap();
let within = tree.nearest_n_within::<SquaredEuclidean>(&[1.0, 2.0, 5.0], 10f64, max_qty, true);

assert_eq!(within.len(), 1);
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impl<A: Axis + Archive<Archived = A>, T: Content + Archive<Archived = T>, const K: usize, const B: usize, IDX: Index<T = IDX> + Archive<Archived = IDX>> ArchivedKdTree<A, T, K, B, IDX>
where usize: Cast<IDX>,

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pub fn nearest_one<D>(&self, query: &[A; K]) -> NearestNeighbour<A, T>
where D: DistanceMetric<A, K>,

Finds the nearest element to query, using the specified distance metric function.

Faster than querying for nearest_n(point, 1, …) due to not needing to allocate memory or maintain sorted results.

§Examples
    use kiddo::KdTree;
    use kiddo::SquaredEuclidean;

    use std::fs::File;
    use memmap::MmapOptions;

    let mmap = unsafe { MmapOptions::new().map(&File::open("./examples/float-doctest-tree.rkyv").expect("./examples/float-doctest-tree.rkyv missing")).unwrap() };
    let tree = unsafe { rkyv::archived_root::<KdTree<f64, 3>>(&mmap) };

    let nearest = tree.nearest_one::<SquaredEuclidean>(&[1.0, 2.0, 5.1]);

    assert!((nearest.distance - 0.01f64).abs() < f64::EPSILON);
    assert_eq!(nearest.item, 100);
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impl<A: Axis + Archive<Archived = A>, T: Content + Archive<Archived = T>, const K: usize, const B: usize, IDX: Index<T = IDX> + Archive<Archived = IDX>> ArchivedKdTree<A, T, K, B, IDX>
where usize: Cast<IDX>,

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pub fn within<D>(&self, query: &[A; K], dist: A) -> Vec<NearestNeighbour<A, T>>
where D: DistanceMetric<A, K>,

Finds all elements within dist of query, using the specified distance metric function.

Results are returned sorted nearest-first

§Examples
    use kiddo::KdTree;
    use kiddo::SquaredEuclidean;
    use std::fs::File;
use memmap::MmapOptions;

let mmap = unsafe { MmapOptions::new().map(&File::open("./examples/float-doctest-tree.rkyv").expect("./examples/float-doctest-tree.rkyv missing")).unwrap() };
let tree = unsafe { rkyv::archived_root::<KdTree<f64, 3>>(&mmap) };

    let within = tree.within::<SquaredEuclidean>(&[1.0, 2.0, 5.0], 10f64);

    assert_eq!(within.len(), 2);
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pub fn within_exclusive<D>( &self, query: &[A; K], dist: A, inclusive: bool, ) -> Vec<NearestNeighbour<A, T>>
where D: DistanceMetric<A, K>,

Finds all elements within dist of query, using the specified distance metric function.

Results are returned sorted nearest-first

§Examples
    use kiddo::KdTree;
    use kiddo::SquaredEuclidean;
    use std::fs::File;
use memmap::MmapOptions;

let mmap = unsafe { MmapOptions::new().map(&File::open("./examples/float-doctest-tree.rkyv").expect("./examples/float-doctest-tree.rkyv missing")).unwrap() };
let tree = unsafe { rkyv::archived_root::<KdTree<f64, 3>>(&mmap) };

    let within = tree.within::<SquaredEuclidean>(&[1.0, 2.0, 5.0], 10f64);

    assert_eq!(within.len(), 2);
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impl<A: Axis + Archive<Archived = A>, T: Content + Archive<Archived = T>, const K: usize, const B: usize, IDX: Index<T = IDX> + Archive<Archived = IDX>> ArchivedKdTree<A, T, K, B, IDX>
where usize: Cast<IDX>,

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pub fn within_unsorted<D>( &self, query: &[A; K], dist: A, ) -> Vec<NearestNeighbour<A, T>>
where D: DistanceMetric<A, K>,

Finds all elements within dist of query, using the specified distance metric function.

Results are returned in arbitrary order. Faster than within.

§Examples
use kiddo::KdTree;
use kiddo::SquaredEuclidean;
use std::fs::File;
use memmap::MmapOptions;

let mmap = unsafe { MmapOptions::new().map(&File::open("./examples/float-doctest-tree.rkyv").expect("./examples/float-doctest-tree.rkyv missing")).unwrap() };
let tree = unsafe { rkyv::archived_root::<KdTree<f64, 3>>(&mmap) };

let within = tree.within_unsorted::<SquaredEuclidean>(&[1.0, 2.0, 5.0], 10f64);

assert_eq!(within.len(), 2);
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pub fn within_unsorted_exclusive<D>( &self, query: &[A; K], dist: A, inclusive: bool, ) -> Vec<NearestNeighbour<A, T>>
where D: DistanceMetric<A, K>,

Finds all elements within dist of query, using the specified distance metric function.

Results are returned in arbitrary order. Faster than within.

§Examples
use kiddo::KdTree;
use kiddo::SquaredEuclidean;
use std::fs::File;
use memmap::MmapOptions;

let mmap = unsafe { MmapOptions::new().map(&File::open("./examples/float-doctest-tree.rkyv").expect("./examples/float-doctest-tree.rkyv missing")).unwrap() };
let tree = unsafe { rkyv::archived_root::<KdTree<f64, 3>>(&mmap) };

let within = tree.within_unsorted::<SquaredEuclidean>(&[1.0, 2.0, 5.0], 10f64);

assert_eq!(within.len(), 2);
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impl<'a, 'query, A: Axis + Archive<Archived = A>, T: Content + Archive<Archived = T>, const K: usize, const B: usize, IDX: Index<T = IDX> + Archive<Archived = IDX> + Send> ArchivedKdTree<A, T, K, B, IDX>
where usize: Cast<IDX>,

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pub fn within_unsorted_iter<D>( &'a self, query: &'query [A; K], dist: A, ) -> WithinUnsortedIter<'a, A, T> ⓘ
where D: DistanceMetric<A, K>,

Finds all elements within dist of query, using the specified distance metric function.

Returns an Iterator. Results are returned in arbitrary order.

§Examples
use kiddo::KdTree;
use kiddo::SquaredEuclidean;
use std::fs::File;
use memmap::MmapOptions;

let mmap = unsafe { MmapOptions::new().map(&File::open("./examples/float-doctest-tree.rkyv").expect("./examples/float-doctest-tree.rkyv missing")).unwrap() };
let tree = unsafe { rkyv::archived_root::<KdTree<f64, 3>>(&mmap) };

let within = tree.within_unsorted_iter::<SquaredEuclidean>(&[1.0, 2.0, 5.0], 10f64).collect::<Vec<_>>();

assert_eq!(within.len(), 2);
Source

pub fn within_unsorted_iter_exclusive<D>( &'a self, query: &'query [A; K], dist: A, inclusive: bool, ) -> WithinUnsortedIter<'a, A, T> ⓘ
where D: DistanceMetric<A, K>,

Finds all elements within dist of query, using the specified distance metric function.

Returns an Iterator. Results are returned in arbitrary order.

§Examples
use kiddo::KdTree;
use kiddo::SquaredEuclidean;
use std::fs::File;
use memmap::MmapOptions;

let mmap = unsafe { MmapOptions::new().map(&File::open("./examples/float-doctest-tree.rkyv").expect("./examples/float-doctest-tree.rkyv missing")).unwrap() };
let tree = unsafe { rkyv::archived_root::<KdTree<f64, 3>>(&mmap) };

let within = tree.within_unsorted_iter::<SquaredEuclidean>(&[1.0, 2.0, 5.0], 10f64).collect::<Vec<_>>();

assert_eq!(within.len(), 2);

Auto Trait Implementations§

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impl<A, T, const K: usize, const B: usize, IDX> !Freeze for ArchivedKdTree<A, T, K, B, IDX>

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impl<A, T, const K: usize, const B: usize, IDX> !RefUnwindSafe for ArchivedKdTree<A, T, K, B, IDX>

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impl<A, T, const K: usize, const B: usize, IDX> !Send for ArchivedKdTree<A, T, K, B, IDX>

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impl<A, T, const K: usize, const B: usize, IDX> !Sync for ArchivedKdTree<A, T, K, B, IDX>

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impl<A, T, const K: usize, const B: usize, IDX> !Unpin for ArchivedKdTree<A, T, K, B, IDX>

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impl<A, T, const K: usize, const B: usize, IDX> !UnsafeUnpin for ArchivedKdTree<A, T, K, B, IDX>

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impl<A, T, const K: usize, const B: usize, IDX> !UnwindSafe for ArchivedKdTree<A, T, K, B, IDX>

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impl<T> Any for T
where T: 'static + ?Sized,

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fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
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impl<T> ArchivePointee for T

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type ArchivedMetadata = ()

The archived version of the pointer metadata for this type.
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fn pointer_metadata( _: &<T as ArchivePointee>::ArchivedMetadata, ) -> <T as Pointee>::Metadata

Converts some archived metadata to the pointer metadata for itself.
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impl<T> ArchivePointee for T

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type ArchivedMetadata = ()

The archived version of the pointer metadata for this type.
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fn pointer_metadata( _: &<T as ArchivePointee>::ArchivedMetadata, ) -> <T as Pointee>::Metadata

Converts some archived metadata to the pointer metadata for itself.
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where T: Cast<Dst>,

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Converts self into a Left variant of Either<Self, Self> if into_left is true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
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