1 files changed, 0 insertions, 546 deletions
diff --git a/src/metric.rs b/src/metric.rs
deleted file mode 100644
index d9938e4..0000000
--- a/src/metric.rs
+++ /dev/null
@@ -1,546 +0,0 @@
-//! [Metric spaces](https://en.wikipedia.org/wiki/Metric_space).
-
-pub mod approx;
-pub mod forest;
-pub mod kd;
-pub mod soft;
-pub mod vp;
-
-use std::cmp::Ordering;
-use std::collections::BinaryHeap;
-use std::iter::FromIterator;
-
-/// A wrapper that converts a partial ordering into a total one by panicking.
-#[derive(Debug, PartialEq, PartialOrd)]
-struct Ordered<T>(T);
-
-impl<T: PartialOrd> Ord for Ordered<T> {
-    fn cmp(&self, other: &Self) -> Ordering {
-        self.partial_cmp(other).unwrap()
-    }
-}
-
-impl<T: PartialEq> Eq for Ordered<T> {}
-
-/// An [order embedding](https://en.wikipedia.org/wiki/Order_embedding) for distances.
-///
-/// Implementations of this trait must satisfy, for all non-negative distances `x` and `y`:
-///
-/// * `x == Self::from(x).into()`
-/// * `x <= y` iff `Self::from(x) <= Self::from(y)`
-///
-/// This trait exists to optimize the common case where distances can be compared more efficiently
-/// than their exact values can be computed.  For example, taking the square root can be avoided
-/// when comparing Euclidean distances (see [SquaredDistance]).
-pub trait Distance: Copy + From<f64> + Into<f64> + PartialOrd {}
-
-impl Distance for f64 {}
-
-/// A squared distance, to avoid computing square roots unless absolutely necessary.
-#[derive(Debug, Clone, Copy, PartialEq, PartialOrd)]
-pub struct SquaredDistance(f64);
-
-impl SquaredDistance {
-    /// Create a SquaredDistance from an already squared value.
-    pub fn from_squared(value: f64) -> Self {
-        Self(value)
-    }
-}
-
-impl From<f64> for SquaredDistance {
-    fn from(value: f64) -> Self {
-        Self::from_squared(value * value)
-    }
-}
-
-impl From<SquaredDistance> for f64 {
-    fn from(value: SquaredDistance) -> Self {
-        value.0.sqrt()
-    }
-}
-
-impl Distance for SquaredDistance {}
-
-/// A [metric space](https://en.wikipedia.org/wiki/Metric_space).
-pub trait Metric<T: ?Sized = Self> {
-    /// The type used to represent distances.
-    type Distance: Distance;
-
-    /// Computes the distance between this point and another point.  This function must satisfy
-    /// three conditions:
-    ///
-    /// * `x.distance(y) == 0` iff `x == y` (identity of indiscernibles)
-    /// * `x.distance(y) == y.distance(x)` (symmetry)
-    /// * `x.distance(z) <= x.distance(y) + y.distance(z)` (triangle inequality)
-    fn distance(&self, other: &T) -> Self::Distance;
-}
-
-/// Blanket [Metric] implementation for references.
-impl<'a, 'b, T, U: Metric<T>> Metric<&'a T> for &'b U {
-    type Distance = U::Distance;
-
-    fn distance(&self, other: &&'a T) -> Self::Distance {
-        (*self).distance(other)
-    }
-}
-
-/// The standard [Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance) metric.
-impl Metric for [f64] {
-    type Distance = SquaredDistance;
-
-    fn distance(&self, other: &Self) -> Self::Distance {
-        debug_assert!(self.len() == other.len());
-
-        let mut sum = 0.0;
-        for i in 0..self.len() {
-            let diff = self[i] - other[i];
-            sum += diff * diff;
-        }
-
-        Self::Distance::from_squared(sum)
-    }
-}
-
-/// A nearest neighbor to a target.
-#[derive(Clone, Copy, Debug, PartialEq)]
-pub struct Neighbor<T> {
-    /// The found item.
-    pub item: T,
-    /// The distance from the target.
-    pub distance: f64,
-}
-
-impl<T> Neighbor<T> {
-    /// Create a new Neighbor.
-    pub fn new(item: T, distance: f64) -> Self {
-        Self { item, distance }
-    }
-}
-
-/// A candidate nearest neighbor found during a search.
-#[derive(Debug)]
-struct Candidate<T, D> {
-    item: T,
-    distance: D,
-}
-
-impl<T, D: Distance> Candidate<T, D> {
-    fn new<U>(target: U, item: T) -> Self
-    where
-        U: Metric<T, Distance = D>,
-    {
-        let distance = target.distance(&item);
-        Self { item, distance }
-    }
-
-    fn into_neighbor(self) -> Neighbor<T> {
-        Neighbor::new(self.item, self.distance.into())
-    }
-}
-
-impl<T, D: Distance> PartialOrd for Candidate<T, D> {
-    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
-        self.distance.partial_cmp(&other.distance)
-    }
-}
-
-impl<T, D: Distance> Ord for Candidate<T, D> {
-    fn cmp(&self, other: &Self) -> Ordering {
-        self.partial_cmp(other).unwrap()
-    }
-}
-
-impl<T, D: Distance> PartialEq for Candidate<T, D> {
-    fn eq(&self, other: &Self) -> bool {
-        self.distance.eq(&other.distance)
-    }
-}
-
-impl<T, D: Distance> Eq for Candidate<T, D> {}
-
-/// Accumulates nearest neighbor search results.
-pub trait Neighborhood<T, U: Metric<T>> {
-    /// Returns the target of the nearest neighbor search.
-    fn target(&self) -> U;
-
-    /// Check whether a distance is within this neighborhood.
-    fn contains(&self, distance: f64) -> bool {
-        distance < 0.0 || self.contains_distance(distance.into())
-    }
-
-    /// Check whether a distance is within this neighborhood.
-    fn contains_distance(&self, distance: U::Distance) -> bool;
-
-    /// Consider a new candidate neighbor.
-    fn consider(&mut self, item: T) -> U::Distance;
-}
-
-/// A [Neighborhood] with at most one result.
-#[derive(Debug)]
-struct SingletonNeighborhood<T, U: Metric<T>> {
-    /// The target of the nearest neighbor search.
-    target: U,
-    /// The current threshold distance to the farthest result.
-    threshold: Option<U::Distance>,
-    /// The current nearest neighbor, if any.
-    candidate: Option<Candidate<T, U::Distance>>,
-}
-
-impl<T, U> SingletonNeighborhood<T, U>
-where
-    U: Copy + Metric<T>,
-{
-    /// Create a new single metric result tracker.
-    ///
-    /// * `target`: The target fo the nearest neighbor search.
-    /// * `threshold`: The maximum allowable distance.
-    fn new(target: U, threshold: Option<f64>) -> Self {
-        Self {
-            target,
-            threshold: threshold.map(U::Distance::from),
-            candidate: None,
-        }
-    }
-
-    /// Consider a candidate.
-    fn push(&mut self, candidate: Candidate<T, U::Distance>) -> U::Distance {
-        let distance = candidate.distance;
-
-        if self.contains_distance(distance) {
-            self.threshold = Some(distance);
-            self.candidate = Some(candidate);
-        }
-
-        distance
-    }
-
-    /// Convert this result into an optional neighbor.
-    fn into_option(self) -> Option<Neighbor<T>> {
-        self.candidate.map(Candidate::into_neighbor)
-    }
-}
-
-impl<T, U> Neighborhood<T, U> for SingletonNeighborhood<T, U>
-where
-    U: Copy + Metric<T>,
-{
-    fn target(&self) -> U {
-        self.target
-    }
-
-    fn contains_distance(&self, distance: U::Distance) -> bool {
-        self.threshold.map(|t| distance <= t).unwrap_or(true)
-    }
-
-    fn consider(&mut self, item: T) -> U::Distance {
-        self.push(Candidate::new(self.target, item))
-    }
-}
-
-/// A [Neighborhood] of up to `k` results, using a binary heap.
-#[derive(Debug)]
-struct HeapNeighborhood<T, U: Metric<T>> {
-    /// The target of the nearest neighbor search.
-    target: U,
-    /// The number of nearest neighbors to find.
-    k: usize,
-    /// The current threshold distance to the farthest result.
-    threshold: Option<U::Distance>,
-    /// A max-heap of the best candidates found so far.
-    heap: BinaryHeap<Candidate<T, U::Distance>>,
-}
-
-impl<T, U> HeapNeighborhood<T, U>
-where
-    U: Copy + Metric<T>,
-{
-    /// Create a new metric result tracker.
-    ///
-    /// * `target`: The target fo the nearest neighbor search.
-    /// * `k`: The number of nearest neighbors to find.
-    /// * `threshold`: The maximum allowable distance.
-    fn new(target: U, k: usize, threshold: Option<f64>) -> Self {
-        Self {
-            target,
-            k,
-            threshold: threshold.map(U::Distance::from),
-            heap: BinaryHeap::with_capacity(k),
-        }
-    }
-
-    /// Consider a candidate.
-    fn push(&mut self, candidate: Candidate<T, U::Distance>) -> U::Distance {
-        let distance = candidate.distance;
-
-        if self.contains_distance(distance) {
-            let heap = &mut self.heap;
-
-            if heap.len() == self.k {
-                heap.pop();
-            }
-
-            heap.push(candidate);
-
-            if heap.len() == self.k {
-                self.threshold = self.heap.peek().map(|c| c.distance)
-            }
-        }
-
-        distance
-    }
-
-    /// Convert these results into a vector of neighbors.
-    fn into_vec(self) -> Vec<Neighbor<T>> {
-        self.heap
-            .into_sorted_vec()
-            .into_iter()
-            .map(Candidate::into_neighbor)
-            .collect()
-    }
-}
-
-impl<T, U> Neighborhood<T, U> for HeapNeighborhood<T, U>
-where
-    U: Copy + Metric<T>,
-{
-    fn target(&self) -> U {
-        self.target
-    }
-
-    fn contains_distance(&self, distance: U::Distance) -> bool {
-        self.k > 0 && self.threshold.map(|t| distance <= t).unwrap_or(true)
-    }
-
-    fn consider(&mut self, item: T) -> U::Distance {
-        self.push(Candidate::new(self.target, item))
-    }
-}
-
-/// A [nearest neighbor search](https://en.wikipedia.org/wiki/Nearest_neighbor_search) index.
-///
-/// Type parameters:
-/// * `T`: The search result type.
-/// * `U`: The query type.
-pub trait NearestNeighbors<T, U: Metric<T> = T> {
-    /// Returns the nearest neighbor to `target` (or `None` if this index is empty).
-    fn nearest(&self, target: &U) -> Option<Neighbor<&T>> {
-        self.search(SingletonNeighborhood::new(target, None))
-            .into_option()
-    }
-
-    /// Returns the nearest neighbor to `target` within the distance `threshold`, if one exists.
-    fn nearest_within(&self, target: &U, threshold: f64) -> Option<Neighbor<&T>> {
-        self.search(SingletonNeighborhood::new(target, Some(threshold)))
-            .into_option()
-    }
-
-    /// Returns the up to `k` nearest neighbors to `target`.
-    fn k_nearest(&self, target: &U, k: usize) -> Vec<Neighbor<&T>> {
-        self.search(HeapNeighborhood::new(target, k, None))
-            .into_vec()
-    }
-
-    /// Returns the up to `k` nearest neighbors to `target` within the distance `threshold`.
-    fn k_nearest_within(&self, target: &U, k: usize, threshold: f64) -> Vec<Neighbor<&T>> {
-        self.search(HeapNeighborhood::new(target, k, Some(threshold)))
-            .into_vec()
-    }
-
-    /// Search for nearest neighbors and add them to a neighborhood.
-    fn search<'a, 'b, N>(&'a self, neighborhood: N) -> N
-    where
-        T: 'a,
-        U: 'b,
-        N: Neighborhood<&'a T, &'b U>;
-}
-
-/// A [NearestNeighbors] implementation that does exhaustive search.
-#[derive(Debug)]
-pub struct ExhaustiveSearch<T>(Vec<T>);
-
-impl<T> ExhaustiveSearch<T> {
-    /// Create an empty ExhaustiveSearch index.
-    pub fn new() -> Self {
-        Self(Vec::new())
-    }
-
-    /// Add a new item to the index.
-    pub fn push(&mut self, item: T) {
-        self.0.push(item);
-    }
-
-    /// Get the size of this index.
-    pub fn len(&self) -> usize {
-        self.0.len()
-    }
-
-    /// Check if this index is empty.
-    pub fn is_empty(&self) -> bool {
-        self.0.is_empty()
-    }
-}
-
-impl<T> Default for ExhaustiveSearch<T> {
-    fn default() -> Self {
-        Self::new()
-    }
-}
-
-impl<T> FromIterator<T> for ExhaustiveSearch<T> {
-    fn from_iter<I: IntoIterator<Item = T>>(items: I) -> Self {
-        Self(items.into_iter().collect())
-    }
-}
-
-impl<T> IntoIterator for ExhaustiveSearch<T> {
-    type Item = T;
-    type IntoIter = std::vec::IntoIter<T>;
-
-    fn into_iter(self) -> Self::IntoIter {
-        self.0.into_iter()
-    }
-}
-
-impl<T> Extend<T> for ExhaustiveSearch<T> {
-    fn extend<I: IntoIterator<Item = T>>(&mut self, iter: I) {
-        for value in iter {
-            self.push(value);
-        }
-    }
-}
-
-impl<T, U: Metric<T>> NearestNeighbors<T, U> for ExhaustiveSearch<T> {
-    fn search<'a, 'b, N>(&'a self, mut neighborhood: N) -> N
-    where
-        T: 'a,
-        U: 'b,
-        N: Neighborhood<&'a T, &'b U>,
-    {
-        for e in &self.0 {
-            neighborhood.consider(e);
-        }
-        neighborhood
-    }
-}
-
-#[cfg(test)]
-pub mod tests {
-    use super::*;
-
-    use rand::prelude::*;
-
-    #[derive(Clone, Copy, Debug, PartialEq)]
-    pub struct Point(pub [f64; 3]);
-
-    impl Metric for Point {
-        type Distance = SquaredDistance;
-
-        fn distance(&self, other: &Self) -> Self::Distance {
-            self.0.distance(&other.0)
-        }
-    }
-
-    /// Test a [NearestNeighbors] impl.
-    pub 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);
-    }
-
-    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 = Point([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![
-            Point([3.0, 4.0, 0.0]),
-            Point([5.0, 0.0, 12.0]),
-            Point([0.0, 8.0, 15.0]),
-            Point([1.0, 2.0, 2.0]),
-            Point([2.0, 3.0, 6.0]),
-            Point([4.0, 4.0, 7.0]),
-        ];
-        let index = from_iter(points);
-        let target = Point([0.0, 0.0, 0.0]);
-
-        assert_eq!(
-            index.nearest(&target),
-            Some(Neighbor::new(&Point([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),
-            Some(Neighbor::new(&Point([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(&Point([1.0, 2.0, 2.0]), 3.0),
-                Neighbor::new(&Point([3.0, 4.0, 0.0]), 5.0),
-                Neighbor::new(&Point([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(&Point([1.0, 2.0, 2.0]), 3.0),
-                Neighbor::new(&Point([3.0, 4.0, 0.0]), 5.0),
-            ]
-        );
-        assert_eq!(
-            index.k_nearest_within(&target, 3, 8.0),
-            vec![
-                Neighbor::new(&Point([1.0, 2.0, 2.0]), 3.0),
-                Neighbor::new(&Point([3.0, 4.0, 0.0]), 5.0),
-                Neighbor::new(&Point([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(Point([random(), random(), random()]));
-        }
-        let target = Point([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]
-    fn test_exhaustive_index() {
-        test_nearest_neighbors(ExhaustiveSearch::from_iter);
-    }
-}