Use the acap nearest neighbors implementationHEADmaster

1 files changed, 292 insertions, 0 deletions

diff --git a/src/forest.rs b/src/forest.rs
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--- /dev/null
+++ b/src/forest.rs
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+//! [Dynamization](https://en.wikipedia.org/wiki/Dynamization) for nearest neighbor search.
+
+use acap::distance::Proximity;
+use acap::kd::FlatKdTree;
+use acap::vp::FlatVpTree;
+use acap::{NearestNeighbors, Neighborhood};
+
+use std::iter::{self, Extend, FromIterator};
+
+/// The number of bits dedicated to the flat buffer.
+const BUFFER_BITS: usize = 6;
+/// The maximum size of the buffer.
+const BUFFER_SIZE: usize = 1 << BUFFER_BITS;
+
+/// A dynamic wrapper for a static nearest neighbor search data structure.
+///
+/// This type applies [dynamization](https://en.wikipedia.org/wiki/Dynamization) to an arbitrary
+/// nearest neighbor search structure `T`, allowing new items to be added dynamically.
+#[derive(Debug)]
+pub struct Forest<T: IntoIterator> {
+    /// A flat buffer used for the first few items, to avoid repeatedly rebuilding small trees.
+    buffer: Vec<T::Item>,
+    /// The trees of the forest, with sizes in geometric progression.
+    trees: Vec<Option<T>>,
+}
+
+impl<T, U> Forest<U>
+where
+    U: FromIterator<T> + IntoIterator<Item = T>,
+{
+    /// Create a new empty forest.
+    pub fn new() -> Self {
+        Self {
+            buffer: Vec::new(),
+            trees: Vec::new(),
+        }
+    }
+
+    /// Add a new item to the forest.
+    pub fn push(&mut self, item: T) {
+        self.extend(iter::once(item));
+    }
+
+    /// Get the number of items in the forest.
+    pub fn len(&self) -> usize {
+        let mut len = self.buffer.len();
+        for (i, slot) in self.trees.iter().enumerate() {
+            if slot.is_some() {
+                len += 1 << (i + BUFFER_BITS);
+            }
+        }
+        len
+    }
+
+    /// Check if this forest is empty.
+    pub fn is_empty(&self) -> bool {
+        if !self.buffer.is_empty() {
+            return false;
+        }
+
+        self.trees.iter().flatten().next().is_none()
+    }
+}
+
+impl<T, U> Default for Forest<U>
+where
+    U: FromIterator<T> + IntoIterator<Item = T>,
+{
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl<T, U> Extend<T> for Forest<U>
+where
+    U: FromIterator<T> + IntoIterator<Item = T>,
+{
+    fn extend<I: IntoIterator<Item = T>>(&mut self, items: I) {
+        self.buffer.extend(items);
+        if self.buffer.len() < BUFFER_SIZE {
+            return;
+        }
+
+        let len = self.len();
+
+        for i in 0.. {
+            let bit = 1 << (i + BUFFER_BITS);
+
+            if bit > len {
+                break;
+            }
+
+            if i >= self.trees.len() {
+                self.trees.push(None);
+            }
+
+            if len & bit == 0 {
+                if let Some(tree) = self.trees[i].take() {
+                    self.buffer.extend(tree);
+                }
+            } else if self.trees[i].is_none() {
+                let offset = self.buffer.len() - bit;
+                self.trees[i] = Some(self.buffer.drain(offset..).collect());
+            }
+        }
+
+        debug_assert!(self.buffer.len() < BUFFER_SIZE);
+        debug_assert!(self.len() == len);
+    }
+}
+
+impl<T, U> FromIterator<T> for Forest<U>
+where
+    U: FromIterator<T> + IntoIterator<Item = T>,
+{
+    fn from_iter<I: IntoIterator<Item = T>>(items: I) -> Self {
+        let mut forest = Self::new();
+        forest.extend(items);
+        forest
+    }
+}
+
+impl<T: IntoIterator> IntoIterator for Forest<T> {
+    type Item = T::Item;
+    type IntoIter = std::vec::IntoIter<T::Item>;
+
+    fn into_iter(mut self) -> Self::IntoIter {
+        self.buffer.extend(self.trees.into_iter().flatten().flatten());
+        self.buffer.into_iter()
+    }
+}
+
+impl<K, V, T> NearestNeighbors<K, V> for Forest<T>
+where
+    K: Proximity<V>,
+    T: NearestNeighbors<K, V>,
+    T: IntoIterator<Item = V>,
+{
+    fn search<'k, 'v, N>(&'v self, mut neighborhood: N) -> N
+    where
+        K: 'k,
+        V: 'v,
+        N: Neighborhood<&'k K, &'v V>
+    {
+        for item in &self.buffer {
+            neighborhood.consider(item);
+        }
+
+        self.trees
+            .iter()
+            .flatten()
+            .fold(neighborhood, |n, t| t.search(n))
+    }
+}
+
+/// A forest of k-d trees.
+pub type KdForest<T> = Forest<FlatKdTree<T>>;
+
+/// A forest of vantage-point trees.
+pub type VpForest<T> = Forest<FlatVpTree<T>>;
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    use acap::euclid::Euclidean;
+    use acap::exhaustive::ExhaustiveSearch;
+    use acap::{NearestNeighbors, Neighbor};
+
+    use rand::prelude::*;
+
+    type Point = Euclidean<[f32; 3]>;
+
+    fn test_empty<T, F>(from_iter: &F)
+    where
+        T: NearestNeighbors<Point>,
+        F: Fn(Vec<Point>) -> T,
+    {
+        let points = Vec::new();
+        let index = from_iter(points);
+        let target = Euclidean([0.0, 0.0, 0.0]);
+        assert_eq!(index.nearest(&target), None);
+        assert_eq!(index.nearest_within(&target, 1.0), None);
+        assert!(index.k_nearest(&target, 0).is_empty());
+        assert!(index.k_nearest(&target, 3).is_empty());
+        assert!(index.k_nearest_within(&target, 0, 1.0).is_empty());
+        assert!(index.k_nearest_within(&target, 3, 1.0).is_empty());
+    }
+
+    fn test_pythagorean<T, F>(from_iter: &F)
+    where
+        T: NearestNeighbors<Point>,
+        F: Fn(Vec<Point>) -> T,
+    {
+        let points = vec![
+            Euclidean([3.0, 4.0, 0.0]),
+            Euclidean([5.0, 0.0, 12.0]),
+            Euclidean([0.0, 8.0, 15.0]),
+            Euclidean([1.0, 2.0, 2.0]),
+            Euclidean([2.0, 3.0, 6.0]),
+            Euclidean([4.0, 4.0, 7.0]),
+        ];
+        let index = from_iter(points);
+        let target = Euclidean([0.0, 0.0, 0.0]);
+
+        assert_eq!(
+            index.nearest(&target).expect("No nearest neighbor found"),
+            Neighbor::new(&Euclidean([1.0, 2.0, 2.0]), 3.0)
+        );
+
+        assert_eq!(index.nearest_within(&target, 2.0), None);
+        assert_eq!(
+            index.nearest_within(&target, 4.0).expect("No nearest neighbor found within 4.0"),
+            Neighbor::new(&Euclidean([1.0, 2.0, 2.0]), 3.0)
+        );
+
+        assert!(index.k_nearest(&target, 0).is_empty());
+        assert_eq!(
+            index.k_nearest(&target, 3),
+            vec![
+                Neighbor::new(&Euclidean([1.0, 2.0, 2.0]), 3.0),
+                Neighbor::new(&Euclidean([3.0, 4.0, 0.0]), 5.0),
+                Neighbor::new(&Euclidean([2.0, 3.0, 6.0]), 7.0),
+            ]
+        );
+
+        assert!(index.k_nearest(&target, 0).is_empty());
+        assert_eq!(
+            index.k_nearest_within(&target, 3, 6.0),
+            vec![
+                Neighbor::new(&Euclidean([1.0, 2.0, 2.0]), 3.0),
+                Neighbor::new(&Euclidean([3.0, 4.0, 0.0]), 5.0),
+            ]
+        );
+        assert_eq!(
+            index.k_nearest_within(&target, 3, 8.0),
+            vec![
+                Neighbor::new(&Euclidean([1.0, 2.0, 2.0]), 3.0),
+                Neighbor::new(&Euclidean([3.0, 4.0, 0.0]), 5.0),
+                Neighbor::new(&Euclidean([2.0, 3.0, 6.0]), 7.0),
+            ]
+        );
+    }
+
+    fn test_random_points<T, F>(from_iter: &F)
+    where
+        T: NearestNeighbors<Point>,
+        F: Fn(Vec<Point>) -> T,
+    {
+        let mut points = Vec::new();
+        for _ in 0..255 {
+            points.push(Euclidean([random(), random(), random()]));
+        }
+        let target = Euclidean([random(), random(), random()]);
+
+        let eindex = ExhaustiveSearch::from_iter(points.clone());
+        let index = from_iter(points);
+
+        assert_eq!(index.k_nearest(&target, 3), eindex.k_nearest(&target, 3));
+    }
+
+    /// Test a [NearestNeighbors] impl.
+    fn test_nearest_neighbors<T, F>(from_iter: F)
+    where
+        T: NearestNeighbors<Point>,
+        F: Fn(Vec<Point>) -> T,
+    {
+        test_empty(&from_iter);
+        test_pythagorean(&from_iter);
+        test_random_points(&from_iter);
+    }
+
+    #[test]
+    fn test_exhaustive_forest() {
+        test_nearest_neighbors(Forest::<ExhaustiveSearch<_>>::from_iter);
+    }
+
+    #[test]
+    fn test_forest_forest() {
+        test_nearest_neighbors(Forest::<Forest<ExhaustiveSearch<_>>>::from_iter);
+    }
+
+    #[test]
+    fn test_kd_forest() {
+        test_nearest_neighbors(KdForest::from_iter);
+    }
+
+    #[test]
+    fn test_vp_forest() {
+        test_nearest_neighbors(VpForest::from_iter);
+    }
+}