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//! [Dynamization](https://en.wikipedia.org/wiki/Dynamization) for nearest neighbor search.
use super::kd::KdTree;
use super::vp::VpTree;
use super::{Metric, 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<T, U, V> NearestNeighbors<T, U> for Forest<V>
where
U: Metric<T>,
V: NearestNeighbors<T, U>,
V: IntoIterator<Item = T>,
{
fn search<'a, 'b, N>(&'a self, mut neighborhood: N) -> N
where
T: 'a,
U: 'b,
N: Neighborhood<&'a T, &'b U>,
{
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<KdTree<T>>;
/// A forest of vantage-point trees.
pub type VpForest<T> = Forest<VpTree<T>>;
#[cfg(test)]
mod tests {
use super::*;
use crate::metric::tests::test_nearest_neighbors;
use crate::metric::ExhaustiveSearch;
#[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);
}
}
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