Use the acap nearest neighbors implementationHEADmaster

1 files changed, 0 insertions, 224 deletions

diff --git a/src/metric/kd.rs b/src/metric/kd.rs
deleted file mode 100644
index 6ea3809..0000000
--- a/src/metric/kd.rs
+++ /dev/null
@@ -1,224 +0,0 @@
-//! [k-d trees](https://en.wikipedia.org/wiki/K-d_tree).
-
-use super::{Metric, NearestNeighbors, Neighborhood, Ordered};
-
-use std::iter::FromIterator;
-
-/// A point in Cartesian space.
-pub trait Cartesian: Metric<[f64]> {
-    /// Returns the number of dimensions necessary to describe this point.
-    fn dimensions(&self) -> usize;
-
-    /// Returns the value of the `i`th coordinate of this point (`i < self.dimensions()`).
-    fn coordinate(&self, i: usize) -> f64;
-}
-
-/// Blanket [Cartesian] implementation for references.
-impl<'a, T: Cartesian> Cartesian for &'a T {
-    fn dimensions(&self) -> usize {
-        (*self).dimensions()
-    }
-
-    fn coordinate(&self, i: usize) -> f64 {
-        (*self).coordinate(i)
-    }
-}
-
-/// Blanket [Metric<[f64]>](Metric) implementation for [Cartesian] references.
-impl<'a, T: Cartesian> Metric<[f64]> for &'a T {
-    type Distance = T::Distance;
-
-    fn distance(&self, other: &[f64]) -> Self::Distance {
-        (*self).distance(other)
-    }
-}
-
-/// Standard cartesian space.
-impl Cartesian for [f64] {
-    fn dimensions(&self) -> usize {
-        self.len()
-    }
-
-    fn coordinate(&self, i: usize) -> f64 {
-        self[i]
-    }
-}
-
-/// Marker trait for cartesian metric spaces.
-pub trait CartesianMetric<T: ?Sized = Self>:
-    Cartesian + Metric<T, Distance = <Self as Metric<[f64]>>::Distance>
-{
-}
-
-/// Blanket [CartesianMetric] implementation for cartesian spaces with compatible metric distance
-/// types.
-impl<T, U> CartesianMetric<T> for U
-where
-    T: ?Sized,
-    U: ?Sized + Cartesian + Metric<T, Distance = <U as Metric<[f64]>>::Distance>,
-{
-}
-
-/// A node in a k-d tree.
-#[derive(Debug)]
-struct KdNode<T> {
-    /// The value stored in this node.
-    item: T,
-    /// The size of the left subtree.
-    left_len: usize,
-}
-
-impl<T: Cartesian> KdNode<T> {
-    /// Create a new KdNode.
-    fn new(item: T) -> Self {
-        Self { item, left_len: 0 }
-    }
-
-    /// Build a k-d tree recursively.
-    fn build(slice: &mut [KdNode<T>], i: usize) {
-        if slice.is_empty() {
-            return;
-        }
-
-        slice.sort_unstable_by_key(|n| Ordered(n.item.coordinate(i)));
-
-        let mid = slice.len() / 2;
-        slice.swap(0, mid);
-
-        let (node, children) = slice.split_first_mut().unwrap();
-        let (left, right) = children.split_at_mut(mid);
-        node.left_len = left.len();
-
-        let j = (i + 1) % node.item.dimensions();
-        Self::build(left, j);
-        Self::build(right, j);
-    }
-
-    /// Recursively search for nearest neighbors.
-    fn recurse<'a, U, N>(
-        slice: &'a [KdNode<T>],
-        i: usize,
-        closest: &mut [f64],
-        neighborhood: &mut N,
-    ) where
-        T: 'a,
-        U: CartesianMetric<&'a T>,
-        N: Neighborhood<&'a T, U>,
-    {
-        let (node, children) = slice.split_first().unwrap();
-        neighborhood.consider(&node.item);
-
-        let target = neighborhood.target();
-        let ti = target.coordinate(i);
-        let ni = node.item.coordinate(i);
-        let j = (i + 1) % node.item.dimensions();
-
-        let (left, right) = children.split_at(node.left_len);
-        let (near, far) = if ti <= ni {
-            (left, right)
-        } else {
-            (right, left)
-        };
-
-        if !near.is_empty() {
-            Self::recurse(near, j, closest, neighborhood);
-        }
-
-        if !far.is_empty() {
-            let saved = closest[i];
-            closest[i] = ni;
-            if neighborhood.contains_distance(target.distance(closest)) {
-                Self::recurse(far, j, closest, neighborhood);
-            }
-            closest[i] = saved;
-        }
-    }
-}
-
-/// A [k-d tree](https://en.wikipedia.org/wiki/K-d_tree).
-#[derive(Debug)]
-pub struct KdTree<T>(Vec<KdNode<T>>);
-
-impl<T: Cartesian> FromIterator<T> for KdTree<T> {
-    /// Create a new k-d tree from a set of points.
-    fn from_iter<I: IntoIterator<Item = T>>(items: I) -> Self {
-        let mut nodes: Vec<_> = items.into_iter().map(KdNode::new).collect();
-        KdNode::build(nodes.as_mut_slice(), 0);
-        Self(nodes)
-    }
-}
-
-impl<T, U> NearestNeighbors<T, U> for KdTree<T>
-where
-    T: Cartesian,
-    U: CartesianMetric<T>,
-{
-    fn search<'a, 'b, N>(&'a self, mut neighborhood: N) -> N
-    where
-        T: 'a,
-        U: 'b,
-        N: Neighborhood<&'a T, &'b U>,
-    {
-        if !self.0.is_empty() {
-            let target = neighborhood.target();
-            let dims = target.dimensions();
-            let mut closest: Vec<_> = (0..dims).map(|i| target.coordinate(i)).collect();
-
-            KdNode::recurse(&self.0, 0, &mut closest, &mut neighborhood);
-        }
-
-        neighborhood
-    }
-}
-
-/// An iterator that the moves values out of a k-d tree.
-#[derive(Debug)]
-pub struct IntoIter<T>(std::vec::IntoIter<KdNode<T>>);
-
-impl<T> Iterator for IntoIter<T> {
-    type Item = T;
-
-    fn next(&mut self) -> Option<T> {
-        self.0.next().map(|n| n.item)
-    }
-}
-
-impl<T> IntoIterator for KdTree<T> {
-    type Item = T;
-    type IntoIter = IntoIter<T>;
-
-    fn into_iter(self) -> Self::IntoIter {
-        IntoIter(self.0.into_iter())
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    use crate::metric::tests::{test_nearest_neighbors, Point};
-    use crate::metric::SquaredDistance;
-
-    impl Metric<[f64]> for Point {
-        type Distance = SquaredDistance;
-
-        fn distance(&self, other: &[f64]) -> Self::Distance {
-            self.0.distance(other)
-        }
-    }
-
-    impl Cartesian for Point {
-        fn dimensions(&self) -> usize {
-            self.0.dimensions()
-        }
-
-        fn coordinate(&self, i: usize) -> f64 {
-            self.0.coordinate(i)
-        }
-    }
-
-    #[test]
-    fn test_kd_tree() {
-        test_nearest_neighbors(KdTree::from_iter);
-    }
-}