From 911ec5008fd78d726cd62768069fa5dbfb4212c0 Mon Sep 17 00:00:00 2001
From: Tavian Barnes <tavianator@tavianator.com>
Date: Thu, 28 May 2020 14:39:08 -0400
Subject: distance: Implement abstract distances, proximities, metrics

---
 src/distance.rs | 103 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 103 insertions(+)
 create mode 100644 src/distance.rs

(limited to 'src/distance.rs')

diff --git a/src/distance.rs b/src/distance.rs
new file mode 100644
index 0000000..9ff9fd4
--- /dev/null
+++ b/src/distance.rs
@@ -0,0 +1,103 @@
+//! Abstract notions of distance.
+
+use num_traits::{Num, NumAssign, Signed};
+
+/// A number type suitable for distance values.
+///
+/// This trait is automatically implemented for all types that support the required operations.
+pub trait Value: Copy + Num + NumAssign + Signed + PartialOrd {}
+
+/// Blanket [Value] implementation.
+impl<T: Num + NumAssign + Signed + Copy + PartialOrd> Value for T {}
+
+/// A distance between two points.
+///
+/// An implementation may be an actual numerical distance, or an [order embedding] of the true
+/// distance.  This allows for optimizations whenever distances can be compared more efficiently
+/// than their exact values can be computed, as is the case for [Euclidean distance].  Implementors
+/// must satisfy, for all distances `x` and `y`:
+///
+/// * `x < y` iff `x.value() < y.value()`
+/// * `x.value() < y` iff `x.value() < y.value()`
+/// * `x < y.value()` iff `x.value() < y.value()`
+///
+/// [order embedding]: https://en.wikipedia.org/wiki/Order_embedding
+/// [Euclidean distance]: crate::euclid::EuclideanDistance
+pub trait Distance
+where
+    Self: Copy,
+    Self: Into<<Self as Distance>::Value>,
+    Self: PartialOrd<<Self as Distance>::Value>,
+    <Self as Distance>::Value: PartialOrd<Self>,
+    Self: PartialOrd,
+{
+    /// The type of actual numerical distances.
+    type Value: Value;
+
+    /// Get the real numerical value of this distance.
+    fn value(self) -> Self::Value {
+        self.into()
+    }
+}
+
+/// Any numerical distance value can be a [Distance].
+impl<T: Value> Distance for T {
+    type Value = T;
+}
+
+/// A space with some notion of distance between points.
+///
+/// Distances in this space don't need to obey any particular rules like symmetry or the [triangle
+/// inequality].  However, spaces that satisfy those rules, at least approximately, often allow for
+/// more accurate and efficient searches.
+///
+/// Type parameters:
+///
+/// * `T`: The type to compare against.
+///
+/// [triangle inequality]: https://en.wikipedia.org/wiki/Triangle_inequality
+pub trait Proximity<T: ?Sized = Self> {
+    /// The type that represents distances.
+    type Distance: Distance;
+
+    /// Calculate the distance between this point and another one.
+    fn distance(&self, other: &T) -> Self::Distance;
+}
+
+// See https://github.com/rust-lang/rust/issues/38078
+/// Shorthand for `K::Distance::Value`.
+pub type DistanceValue<K, V = K> = <<K as Proximity<V>>::Distance as Distance>::Value;
+
+/// Blanket [Proximity] implementation for references.
+impl<'k, 'v, K: Proximity<V>, V> Proximity<&'v V> for &'k K {
+    type Distance = K::Distance;
+
+    fn distance(&self, other: &&'v V) -> Self::Distance {
+        (*self).distance(*other)
+    }
+}
+
+/// Marker trait for [metric spaces].
+///
+/// A metric must be symmetric and obey the [triangle inequality].  More precisely, let `x`, `y`,
+/// and `z` be any elements of a metric space, and let `d(x, y) = x.distance(y).value()`.  Then the
+/// following rules must hold:
+///
+/// * `d(x, x) == 0`,
+/// * `d(x, y) == d(y, z)` (symmetry), and
+/// * `d(x, z) <= d(x, y) + d(y, z)` (triangle inequality).
+///
+/// Those conditions also imply the following condition:
+///
+/// * `d(x, y) >= 0` (non-negativity)
+///
+/// Because we do not prohibit `d(x, y) == 0` for distinct `x` and `y`, these spaces are more
+/// properly known as [pseudometric spaces].  This distinction is usually unimportant.
+///
+/// [metric spaces]: https://en.wikipedia.org/wiki/Metric_space
+/// [triangle inequality]: https://en.wikipedia.org/wiki/Triangle_inequality
+/// [pseudometric spaces]: https://en.wikipedia.org/wiki/Pseudometric_space
+pub trait Metric<T: ?Sized = Self>: Proximity<T> {}
+
+/// Blanket [Metric] implementation for references.
+impl<'k, 'v, K: Metric<V>, V> Metric<&'v V> for &'k K {}
-- 
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