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-rw-r--r--src/metric.rs1
-rw-r--r--src/metric/approx.rs131
2 files changed, 132 insertions, 0 deletions
diff --git a/src/metric.rs b/src/metric.rs
index b46c8da..268aefd 100644
--- a/src/metric.rs
+++ b/src/metric.rs
@@ -1,5 +1,6 @@
//! [Metric spaces](https://en.wikipedia.org/wiki/Metric_space).
+pub mod approx;
pub mod forest;
pub mod kd;
pub mod soft;
diff --git a/src/metric/approx.rs b/src/metric/approx.rs
new file mode 100644
index 0000000..c23f9c7
--- /dev/null
+++ b/src/metric/approx.rs
@@ -0,0 +1,131 @@
+//! [Approximate nearest neighbor search](https://en.wikipedia.org/wiki/Nearest_neighbor_search#Approximate_nearest_neighbor).
+
+use super::{Metric, NearestNeighbors, Neighborhood};
+
+/// An approximate [Neighborhood], for approximate nearest neighbor searches.
+#[derive(Debug)]
+struct ApproximateNeighborhood<N> {
+ inner: N,
+ ratio: f64,
+ limit: usize,
+}
+
+impl<N> ApproximateNeighborhood<N> {
+ fn new(inner: N, ratio: f64, limit: usize) -> Self {
+ Self {
+ inner,
+ ratio,
+ limit,
+ }
+ }
+}
+
+impl<T, U, N> Neighborhood<T, U> for ApproximateNeighborhood<N>
+where
+ U: Metric<T>,
+ N: Neighborhood<T, U>,
+{
+ fn target(&self) -> U {
+ self.inner.target()
+ }
+
+ fn contains(&self, distance: f64) -> bool {
+ if self.limit > 0 {
+ self.inner.contains(self.ratio * distance)
+ } else {
+ false
+ }
+ }
+
+ fn contains_distance(&self, distance: U::Distance) -> bool {
+ self.contains(self.ratio * distance.into())
+ }
+
+ fn consider(&mut self, item: T) -> U::Distance {
+ self.limit = self.limit.saturating_sub(1);
+ self.inner.consider(item)
+ }
+}
+
+/// An [approximate nearest neighbor search](https://en.wikipedia.org/wiki/Nearest_neighbor_search#Approximate_nearest_neighbor)
+/// index.
+///
+/// This wrapper converts an exact nearest neighbor search algorithm into an approximate one by
+/// modifying the behavior of [Neighborhood::contains]. The approximation is controlled by two
+/// parameters:
+///
+/// * `ratio`: The [nearest neighbor distance ratio](https://en.wikipedia.org/wiki/Nearest_neighbor_search#Nearest_neighbor_distance_ratio),
+/// which controls how much closer new candidates must be to be considered. For example, a ratio
+/// of 2.0 means that a neighbor must be less than half of the current threshold away to be
+/// considered. A ratio of 1.0 means an exact search.
+///
+/// * `limit`: A limit on the number of candidates to consider. Typical nearest neighbor algorithms
+/// find a close match quickly, so setting a limit bounds the worst-case search time while keeping
+/// good accuracy.
+#[derive(Debug)]
+pub struct ApproximateSearch<T> {
+ inner: T,
+ ratio: f64,
+ limit: usize,
+}
+
+impl<T> ApproximateSearch<T> {
+ /// Create a new ApproximateSearch index.
+ ///
+ /// * `inner`: The [NearestNeighbors] implementation to wrap.
+ /// * `ratio`: The nearest neighbor distance ratio.
+ /// * `limit`: The maximum number of results to consider.
+ pub fn new(inner: T, ratio: f64, limit: usize) -> Self {
+ Self {
+ inner,
+ ratio,
+ limit,
+ }
+ }
+}
+
+impl<T, U, V> NearestNeighbors<T, U> for ApproximateSearch<V>
+where
+ U: Metric<T>,
+ V: NearestNeighbors<T, U>,
+{
+ fn search<'a, 'b, N>(&'a self, neighborhood: N) -> N
+ where
+ T: 'a,
+ U: 'b,
+ N: Neighborhood<&'a T, &'b U>,
+ {
+ self.inner
+ .search(ApproximateNeighborhood::new(
+ neighborhood,
+ self.ratio,
+ self.limit,
+ ))
+ .inner
+ }
+}
+
+#[cfg(test)]
+mod tests {
+ use super::*;
+
+ use crate::metric::kd::KdTree;
+ use crate::metric::tests::test_nearest_neighbors;
+ use crate::metric::vp::VpTree;
+
+ use std::iter::FromIterator;
+
+ #[test]
+ fn test_approx_kd_tree() {
+ test_nearest_neighbors(|iter| {
+ ApproximateSearch::new(KdTree::from_iter(iter), 1.0, std::usize::MAX)
+ });
+ }
+
+ #[test]
+ fn test_approx_vp_tree() {
+ test_nearest_neighbors(|iter| {
+ ApproximateSearch::new(VpTree::from_iter(iter), 1.0, std::usize::MAX)
+ });
+ }
+}