1 files changed, 0 insertions, 288 deletions
diff --git a/src/soft.rs b/src/soft.rs
deleted file mode 100644
index 4ce4204..0000000
--- a/src/soft.rs
+++ /dev/null
@@ -1,288 +0,0 @@
-//! [Soft deletion](https://en.wiktionary.org/wiki/soft_deletion) for nearest neighbor search.
-
-use super::forest::{KdForest, VpForest};
-
-use acap::distance::Proximity;
-use acap::kd::FlatKdTree;
-use acap::knn::{NearestNeighbors, Neighborhood};
-use acap::vp::FlatVpTree;
-
-use std::iter;
-use std::mem;
-
-/// A trait for objects that can be soft-deleted.
-pub trait SoftDelete {
-    /// Check whether this item is deleted.
-    fn is_deleted(&self) -> bool;
-}
-
-/// Blanket [SoftDelete] implementation for references.
-impl<'a, T: SoftDelete> SoftDelete for &'a T {
-    fn is_deleted(&self) -> bool {
-        (*self).is_deleted()
-    }
-}
-
-/// [Neighborhood] wrapper that ignores soft-deleted items.
-#[derive(Debug)]
-struct SoftNeighborhood<N>(N);
-
-impl<K, V, N> Neighborhood<K, V> for SoftNeighborhood<N>
-where
-    V: SoftDelete,
-    K: Proximity<V>,
-    N: Neighborhood<K, V>,
-{
-    fn target(&self) -> K {
-        self.0.target()
-    }
-
-    fn contains<D>(&self, distance: D) -> bool
-    where
-        D: PartialOrd<K::Distance>
-    {
-        self.0.contains(distance)
-    }
-
-    fn consider(&mut self, item: V) -> K::Distance {
-        if item.is_deleted() {
-            self.target().distance(&item)
-        } else {
-            self.0.consider(item)
-        }
-    }
-}
-
-/// A [NearestNeighbors] implementation that supports [soft deletes](https://en.wiktionary.org/wiki/soft_deletion).
-#[derive(Debug)]
-pub struct SoftSearch<T>(T);
-
-impl<T, U> SoftSearch<U>
-where
-    T: SoftDelete,
-    U: FromIterator<T> + IntoIterator<Item = T>,
-{
-    /// Create a new empty soft index.
-    pub fn new() -> Self {
-        Self(iter::empty().collect())
-    }
-
-    /// Push a new item into this index.
-    pub fn push(&mut self, item: T)
-    where
-        U: Extend<T>,
-    {
-        self.0.extend(iter::once(item));
-    }
-
-    /// Rebuild this index, discarding deleted items.
-    pub fn rebuild(&mut self) {
-        let items = mem::replace(&mut self.0, iter::empty().collect());
-        self.0 = items.into_iter().filter(|e| !e.is_deleted()).collect();
-    }
-}
-
-impl<T, U> Default for SoftSearch<U>
-where
-    T: SoftDelete,
-    U: FromIterator<T> + IntoIterator<Item = T>,
-{
-    fn default() -> Self {
-        Self::new()
-    }
-}
-
-impl<T, U: Extend<T>> Extend<T> for SoftSearch<U> {
-    fn extend<I: IntoIterator<Item = T>>(&mut self, iter: I) {
-        self.0.extend(iter);
-    }
-}
-
-impl<T, U: FromIterator<T>> FromIterator<T> for SoftSearch<U> {
-    fn from_iter<I: IntoIterator<Item = T>>(iter: I) -> Self {
-        Self(U::from_iter(iter))
-    }
-}
-
-impl<T: IntoIterator> IntoIterator for SoftSearch<T> {
-    type Item = T::Item;
-    type IntoIter = T::IntoIter;
-
-    fn into_iter(self) -> Self::IntoIter {
-        self.0.into_iter()
-    }
-}
-
-impl<K, V, T> NearestNeighbors<K, V> for SoftSearch<T>
-where
-    K: Proximity<V>,
-    V: SoftDelete,
-    T: NearestNeighbors<K, V>,
-{
-    fn search<'k, 'v, N>(&'v self, neighborhood: N) -> N
-    where
-        K: 'k,
-        V: 'v,
-        N: Neighborhood<&'k K, &'v V>
-    {
-        self.0.search(SoftNeighborhood(neighborhood)).0
-    }
-}
-
-/// A k-d forest that supports soft deletes.
-pub type SoftKdForest<T> = SoftSearch<KdForest<T>>;
-
-/// A k-d tree that supports soft deletes.
-pub type SoftKdTree<T> = SoftSearch<FlatKdTree<T>>;
-
-/// A VP forest that supports soft deletes.
-pub type SoftVpForest<T> = SoftSearch<VpForest<T>>;
-
-/// A VP tree that supports soft deletes.
-pub type SoftVpTree<T> = SoftSearch<FlatVpTree<T>>;
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    use acap::coords::Coordinates;
-    use acap::euclid::{euclidean_distance, Euclidean, EuclideanDistance};
-    use acap::knn::Neighbor;
-
-    type Point = Euclidean<[f32; 3]>;
-
-    #[derive(Debug, PartialEq)]
-    struct SoftPoint {
-        point: [f32; 3],
-        deleted: bool,
-    }
-
-    impl SoftPoint {
-        fn new(x: f32, y: f32, z: f32) -> Self {
-            Self {
-                point: [x, y, z],
-                deleted: false,
-            }
-        }
-
-        fn deleted(x: f32, y: f32, z: f32) -> Self {
-            Self {
-                point: [x, y, z],
-                deleted: true,
-            }
-        }
-    }
-
-    impl SoftDelete for SoftPoint {
-        fn is_deleted(&self) -> bool {
-            self.deleted
-        }
-    }
-
-    impl Proximity for SoftPoint {
-        type Distance = EuclideanDistance<f32>;
-
-        fn distance(&self, other: &Self) -> Self::Distance {
-            euclidean_distance(&self.point, &other.point)
-        }
-    }
-
-    impl Coordinates for SoftPoint {
-        type Value = <Point as Coordinates>::Value;
-
-        fn dims(&self) -> usize {
-            self.point.dims()
-        }
-
-        fn coord(&self, i: usize) -> Self::Value {
-            self.point.coord(i)
-        }
-    }
-
-    impl Proximity<SoftPoint> for Point {
-        type Distance = EuclideanDistance<f32>;
-
-        fn distance(&self, other: &SoftPoint) -> Self::Distance {
-            euclidean_distance(&self, &other.point)
-        }
-    }
-
-    fn test_index<T>(index: &T)
-    where
-        T: NearestNeighbors<Point, SoftPoint>,
-    {
-        let target = Euclidean([0.0, 0.0, 0.0]);
-
-        assert_eq!(
-            index.nearest(&target).expect("No nearest neighbor found"),
-            Neighbor::new(&SoftPoint::new(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(&SoftPoint::new(1.0, 2.0, 2.0), 3.0)
-        );
-
-        assert_eq!(
-            index.k_nearest(&target, 3),
-            vec![
-                Neighbor::new(&SoftPoint::new(1.0, 2.0, 2.0), 3.0),
-                Neighbor::new(&SoftPoint::new(3.0, 4.0, 0.0), 5.0),
-                Neighbor::new(&SoftPoint::new(2.0, 3.0, 6.0), 7.0),
-            ]
-        );
-
-        assert_eq!(
-            index.k_nearest_within(&target, 3, 6.0),
-            vec![
-                Neighbor::new(&SoftPoint::new(1.0, 2.0, 2.0), 3.0),
-                Neighbor::new(&SoftPoint::new(3.0, 4.0, 0.0), 5.0),
-            ]
-        );
-        assert_eq!(
-            index.k_nearest_within(&target, 3, 8.0),
-            vec![
-                Neighbor::new(&SoftPoint::new(1.0, 2.0, 2.0), 3.0),
-                Neighbor::new(&SoftPoint::new(3.0, 4.0, 0.0), 5.0),
-                Neighbor::new(&SoftPoint::new(2.0, 3.0, 6.0), 7.0),
-            ]
-        );
-    }
-
-    fn test_soft_index<T>(index: &mut SoftSearch<T>)
-    where
-        T: Extend<SoftPoint>,
-        T: FromIterator<SoftPoint>,
-        T: IntoIterator<Item = SoftPoint>,
-        T: NearestNeighbors<Point, SoftPoint>,
-    {
-        let points = vec![
-            SoftPoint::deleted(0.0, 0.0, 0.0),
-            SoftPoint::new(3.0, 4.0, 0.0),
-            SoftPoint::new(5.0, 0.0, 12.0),
-            SoftPoint::new(0.0, 8.0, 15.0),
-            SoftPoint::new(1.0, 2.0, 2.0),
-            SoftPoint::new(2.0, 3.0, 6.0),
-            SoftPoint::new(4.0, 4.0, 7.0),
-        ];
-
-        for point in points {
-            index.push(point);
-        }
-        test_index(index);
-
-        index.rebuild();
-        test_index(index);
-    }
-
-    #[test]
-    fn test_soft_kd_forest() {
-        test_soft_index(&mut SoftKdForest::new());
-    }
-
-    #[test]
-    fn test_soft_vp_forest() {
-        test_soft_index(&mut SoftVpForest::new());
-    }
-}