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use std::collections::BinaryHeap;
use std::cmp::Ordering;
/// A `Point` is something that exists in some sort of metric space, and
/// can thus calculate its distance to another `Point`, and can be moved
/// a certain distance towards another `Point`.
pub trait Point : Sized {
/// Distances should be positive, finite `f64`s. It is undefined behavior to
/// return a negative, infinite, or `NaN` result.
///
/// Distance should satisfy the triangle inequality. That is, `a.distance(c)`
/// must be less or equal to than `a.distance(b) + b.distance(c)`.
fn distance(&self, other: &Self) -> f64;
/// If `d` is `0`, a point equal to the `self` should be returned. If `d` is equal
/// to `self.distance(other)`, a point equal to `other` should be returned.
/// Intermediate distances should be linearly interpolated between the two points,
/// so if `d` is equal to `self.distance(other) / 2.0`, the midpoint should be
/// returned.
/// It is undefined behavior to use a distance that is negative, `NaN`, or greater
/// than `self.distance(other)`.
fn move_towards(&self, other: &Self, d: f64) -> Self;
}
fn midpoint<P: Point>(a: &P, b: &P) -> P {
let d = a.distance(b);
a.move_towards(b, d / 2.0)
}
// A little helper to allow us to use comparative functions on `f64`s by asserting that
// `NaN` isn't present.
#[derive(PartialEq, PartialOrd)]
struct OrdF64(f64);
impl OrdF64 {
fn new(x: f64) -> Self {
assert!(!x.is_nan());
OrdF64(x)
}
}
impl Eq for OrdF64 {}
impl Ord for OrdF64 {
fn cmp(&self, other: &Self) -> Ordering {
self.partial_cmp(other).unwrap()
}
}
struct Sphere<C> {
center: C,
radius: f64,
}
// Implementation of the "bouncing bubble" algorithm which essentially works like this:
// * Pick a point `a` that is farthest from `points[0]`
// * Pick a point `b` that is farthest from `a`
// * Use these two points to create an initial sphere centered at their midpoint and with
// enough radius to encompass them
// * While there is still a point outside of this sphere, move the sphere towards that
// point just enough to encompass that point, and grow the sphere radius by 1%
//
// This process will produce a non-optimal, but relatively snug fitting bounding sphere.
fn bounding_sphere<P: Point>(points: &[P]) -> Sphere<P> {
assert!(points.len() >= 2);
let a = &points
.iter()
.max_by_key(|a| OrdF64::new(points[0].distance(a)))
.unwrap();
let b = &points
.iter()
.max_by_key(|b| OrdF64::new(a.distance(b)))
.unwrap();
let mut center: P = midpoint(a, b);
let mut radius = center.distance(b);
loop {
match points.iter().filter(|p| center.distance(p) > radius).next() {
None => break Sphere { center, radius },
Some(p) => {
let c_to_p = center.distance(&p);
let d = c_to_p - radius;
center = center.move_towards(p, d);
radius = radius * 1.01;
},
}
}
}
// Produce a partition of the given points with the following process:
// * Pick a point `a` that is farthest from `points[0]`
// * Pick a point `b` that is farthest from `a`
// * Partition the points into two groups: those closest to `a` and those closest to `b`
//
// This doesn't necessarily form the best partition, since `a` and `b` are not guaranteed
// to be the most distance pair of points, but it's usually sufficient.
fn partition<P: Point, V>(mut points: Vec<P>, mut values: Vec<V>) -> ((Vec<P>, Vec<V>), (Vec<P>, Vec<V>)) {
assert!(points.len() >= 2);
assert_eq!(points.len(), values.len());
let a_i = points
.iter()
.enumerate()
.max_by_key(|(_,a)| OrdF64::new(points[0].distance(a)))
.unwrap().0;
let b_i = points
.iter()
.enumerate()
.max_by_key(|(_,b)| OrdF64::new(points[a_i].distance(b)))
.unwrap().0;
let (a_i, b_i) = (a_i.max(b_i), a_i.min(b_i));
let (mut aps, mut avs) = (vec![points.swap_remove(a_i)], vec![values.swap_remove(a_i)]);
let (mut bps, mut bvs) = (vec![points.swap_remove(b_i)], vec![values.swap_remove(b_i)]);
for (p,v) in points.into_iter().zip(values) {
if aps[0].distance(&p) < bps[0].distance(&p) {
aps.push(p);
avs.push(v);
} else {
bps.push(p);
bvs.push(v);
}
}
((aps, avs), (bps, bvs))
}
// We could add a `Null` variant to support empty trees, but would that actually be used/useful?
enum BallTreeInner<P, V> {
Leaf(P, V),
// The sphere is a bounding sphere that encompasses this node (both children)
Branch(Sphere<P>, Box<BallTreeInner<P, V>>, Box<BallTreeInner<P, V>>),
}
impl <P: Point, V> BallTreeInner<P, V> {
fn new(mut points: Vec<P>, mut values: Vec<V>) -> Self {
assert!(points.len() > 0, "Cannot construct a ball-tree with zero points");
assert_eq!(
points.len(), values.len(),
"Given two vectors of differing lengths. points: {}, values: {}",
points.len(),
values.len()
);
if points.len() == 1 {
let (p, v) = (points.swap_remove(0), values.swap_remove(0));
BallTreeInner::Leaf(p, v)
} else {
let sphere = bounding_sphere(&points);
let ((aps, avs), (bps, bvs)) = partition(points, values);
let (a_tree, b_tree) = (BallTreeInner::new(aps, avs), BallTreeInner::new(bps, bvs));
BallTreeInner::Branch(sphere, Box::new(a_tree), Box::new(b_tree))
}
}
fn distance(&self, p: &P) -> f64 {
match self {
// The distance to a leaf is the distance to the single point inside of it
BallTreeInner::Leaf(p0, _) => p.distance(p0),
// The distance to a branch is the distance to the edge of the bounding sphere
BallTreeInner::Branch(sphere, _, _) => p.distance(&sphere.center) - sphere.radius
}
}
// Maintain a priority queue of the nodes that are closest to the provided `point`. If we
// pop a leaf from the queue, that leaf is necessarily the next closest point. If we
// pop a branch from the queue, add its children. The priority of a node is its
// `distance` as defined above.
fn knn(&self, point: &P, mut k: usize, mut result: impl FnMut(&P, f64, &V)) {
// We need a little wrapper to hold our priority queue elements for two reasons:
// * Rust's BinaryHeap is a max-heap, and we need a min-heap, so we invert the
// ordering
// * We only want to order based on the first element, so we need a custom
// implementation rather than deriving the order (which would require the value
// to be orderable which is not necessary).
struct Item<T>(f64, T);
impl <T> PartialEq for Item<T> {
fn eq(&self, other: &Self) -> bool {
self.0 == other.0
}
}
impl <T> Eq for Item<T> {}
impl <T> PartialOrd for Item<T> {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
self.0.partial_cmp(&other.0).map(|ordering| ordering.reverse())
}
}
impl <T> Ord for Item<T> {
fn cmp(&self, other: &Self) -> Ordering {
self.partial_cmp(other).unwrap()
}
}
// Initialize the queue with our children as our only elements
let mut balls: BinaryHeap<_> = vec![Item(0.0, self)].into();
while let Some(Item(d, ball)) = balls.pop() {
if k == 0 {
break;
}
match ball {
BallTreeInner::Leaf(p, v) => {
result(p, d, v);
k -= 1;
},
BallTreeInner::Branch(_, a, b) => {
let d_a = a.distance(point);
let d_b = b.distance(point);
balls.push(Item(d_a, a));
balls.push(Item(d_b, b));
},
}
}
}
}
/// A `BallTree` is a space-partitioning data-structure that allows for finding
/// nearest neighbors in logarithmic time.
///
/// It does this by partitioning data into a series of nested bounding spheres
/// ("balls" in the literature). Spheres are used because it is trivial to
/// compute the distance between a point and a sphere (distance to the sphere's
/// center minus thte radius). The key observation is that a potential neighbor
/// is necessarily closer than all neighbors that are located inside of a
/// bounding sphere that is farther than the aforementioned neighbor.
///
/// Graphically:
/// ```text
///
/// A -
/// | ---- distance(A, B) = 4
/// | - B distance(A, S) = 6
/// |
/// |
/// | S
/// --------
/// / G \
/// / C \
/// | D |
/// | F |
/// \ E /
/// \_________/
///```
///
/// In the diagram, `A` is closer to `B` than to `S`, and because `S` bounds
/// `C`, `D`, `E`, `F`, and `G`, it can be determined that `A` it is necessarily
/// closer to `B` than the other points without even computing exact distances
/// to them.
///
/// Ball trees are most commonly used as a form of predictive model where the
/// points are features and each point is associated with a value or label. Thus,
/// This implementation allows the user to associate a value with each point. If
/// this functionality is unneeded, `()` can be used as a value.
///
/// This implementation returns the nearest neighbors, their distances, and their
/// associated values. Returning the distances allows the user to perform some
/// sort of weighted interpolation of the neighbors for predictive purposes.
pub struct BallTree<P, V>(BallTreeInner<P, V>);
impl <P: Point, V> BallTree<P, V> {
/// Construct this `BallTree`. Construction is somewhat expensive, so `BallTree`s
/// are best constructed once and then used repeatedly.
///
/// `panic` if `points.len() != values.len()`
pub fn new(points: Vec<P>, values: Vec<V>) -> Self {
BallTree(BallTreeInner::new(points, values))
}
/// Given a `point` and a number of neigbors to look for, find the `k` nearest
/// neighbors (or fewer, if the ball tree contains fewer than `k` points).
///
/// The neighbor, its distance, and associated value is returned.
///
/// We allow the caller to pass in a result-consuming closure rather than
/// return a `Vec` to allow the caller to control allocation.
pub fn knn(&self, point: &P, k: usize, result: impl FnMut(&P, f64, &V)) {
self.0.knn(point, k, result)
}
}
#[cfg(test)]
mod tests {
use super::*;
#[derive(Debug, Clone, Copy, PartialEq)]
struct TestPoint(f64);
impl Point for TestPoint {
fn distance(&self, other: &Self) -> f64 {
(self.0 - other.0).abs()
}
fn move_towards(&self, other: &Self, d: f64) -> Self {
if self.0 > other.0 {
TestPoint(self.0 - d)
} else {
TestPoint(self.0 + d)
}
}
}
#[test]
fn test() {
let points = (0_u32..1000).map(|x| TestPoint(x as f64)).collect::<Vec<_>>();
let values = (0_u32..1000).map(|x| x.pow(2)).collect::<Vec<_>>();
let tree = BallTree::new(points.clone(), values.clone());
let n = 10;
for p in vec![123.4, 567.8, 99999.9] {
let mut results = vec![];
tree.knn(&TestPoint(p), n, |k, d, v| results.push((*k, d, *v)));
let mut reference = points
.clone()
.into_iter()
.zip(values.clone())
.map(|(k,v)| (k, (k.0 - p).abs(), v))
.collect::<Vec<_>>();
reference.sort_by_key(|(_, d, _)| OrdF64::new(*d));
let expected_results = reference
.into_iter()
.take(n)
.collect::<Vec<_>>();
assert_eq!(expected_results, results);
}
}
}