path: root/src/forest.rs

//! [Dynamization](https://en.wikipedia.org/wiki/Dynamization) for nearest neighbor search.

use acap::distance::Proximity;
use acap::kd::FlatKdTree;
use acap::knn::{NearestNeighbors, Neighborhood};
use acap::vp::FlatVpTree;

use std::iter;

/// 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::knn::{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);
    }
}