diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/metric.rs | 1 | ||||
| -rw-r--r-- | src/metric/kd.rs | 217 |
2 files changed, 218 insertions, 0 deletions
diff --git a/src/metric.rs b/src/metric.rs index 549db67..95191da 100644 --- a/src/metric.rs +++ b/src/metric.rs @@ -1,5 +1,6 @@ //! [Metric spaces](https://en.wikipedia.org/wiki/Metric_space). +pub mod kd; pub mod vp; use ordered_float::OrderedFloat; diff --git a/src/metric/kd.rs b/src/metric/kd.rs new file mode 100644 index 0000000..ab0cd2e --- /dev/null +++ b/src/metric/kd.rs @@ -0,0 +1,217 @@ +//! [k-d trees](https://en.wikipedia.org/wiki/K-d_tree). + +use super::{Metric, NearestNeighbors, Neighborhood}; + +use ordered_float::OrderedFloat; + +use std::iter::FromIterator; + +/// A point in Cartesian space. +pub trait Cartesian { + /// 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) + } +} + +/// Standard cartesian space. +impl Cartesian for [f64] { + fn dimensions(&self) -> usize { + self.len() + } + + fn coordinate(&self, i: usize) -> f64 { + self[i] + } +} + +/// A node in a k-d tree. +#[derive(Debug)] +struct KdNode<T> { + /// The value stored in this node. + item: T, + /// The left subtree, if any. + left: Option<Box<Self>>, + /// The right subtree, if any. + right: Option<Box<Self>>, +} + +trait KdSearch<'a, T, U, N> { + /// Recursively search for nearest neighbors. + fn search(&'a self, i: usize, neighborhood: &mut N); + + /// Search the left subtree. + fn search_left(&'a self, i: usize, distance: f64, neighborhood: &mut N); + + /// Search the right subtree. + fn search_right(&'a self, i: usize, distance: f64, neighborhood: &mut N); +} + +impl<'a, T, U, N> KdSearch<'a, T, U, N> for KdNode<T> +where + T: 'a + Cartesian, + U: Cartesian + Metric<&'a T>, + N: Neighborhood<&'a T, U>, +{ + fn search(&'a self, i: usize, neighborhood: &mut N) { + neighborhood.consider(&self.item); + + let distance = neighborhood.target().coordinate(i) - self.item.coordinate(i); + let j = (i + 1) % self.item.dimensions(); + if distance <= 0.0 { + self.search_left(j, distance, neighborhood); + self.search_right(j, -distance, neighborhood); + } else { + self.search_right(j, -distance, neighborhood); + self.search_left(j, distance, neighborhood); + } + } + + fn search_left(&'a self, i: usize, distance: f64, neighborhood: &mut N) { + if let Some(left) = &self.left { + if neighborhood.contains(distance) { + left.search(i, neighborhood); + } + } + } + + fn search_right(&'a self, i: usize, distance: f64, neighborhood: &mut N) { + if let Some(right) = &self.right { + if neighborhood.contains(distance) { + right.search(i, neighborhood); + } + } + } +} + +impl<T: Cartesian> KdNode<T> { + /// Create a new KdNode. + fn new(i: usize, mut items: Vec<T>) -> Option<Box<Self>> { + if items.is_empty() { + return None; + } + + items.sort_unstable_by_key(|x| OrderedFloat::from(x.coordinate(i))); + + let mid = items.len() / 2; + let right: Vec<T> = items.drain((mid + 1)..).collect(); + let item = items.pop().unwrap(); + let j = (i + 1) % item.dimensions(); + Some(Box::new(Self { + item, + left: Self::new(j, items), + right: Self::new(j, right), + })) + } +} + +/// A [k-d tree](https://en.wikipedia.org/wiki/K-d_tree). +#[derive(Debug)] +pub struct KdTree<T> { + root: Option<Box<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 { + Self { + root: KdNode::new(0, items.into_iter().collect()), + } + } +} + +impl<T, U> NearestNeighbors<T, U> for KdTree<T> +where + T: Cartesian, + U: Cartesian + Metric<T>, +{ + fn search<'a, 'b, N>(&'a self, mut neighborhood: N) -> N + where + T: 'a, + U: 'b, + N: Neighborhood<&'a T, &'b U>, + { + if let Some(root) = &self.root { + root.search(0, &mut neighborhood); + } + neighborhood + } +} + +/// An iterator that the moves values out of a k-d tree. +#[derive(Debug)] +pub struct IntoIter<T> { + stack: Vec<Box<KdNode<T>>>, +} + +impl<T> IntoIter<T> { + fn new(node: Option<Box<KdNode<T>>>) -> Self { + Self { + stack: node.into_iter().collect(), + } + } +} + +impl<T> Iterator for IntoIter<T> { + type Item = T; + + fn next(&mut self) -> Option<T> { + self.stack.pop().map(|node| { + self.stack.extend(node.left); + self.stack.extend(node.right); + node.item + }) + } +} + +impl<T> IntoIterator for KdTree<T> { + type Item = T; + type IntoIter = IntoIter<T>; + + fn into_iter(self) -> Self::IntoIter { + IntoIter::new(self.root) + } +} + +#[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); + } +} |