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author | Tavian Barnes <tavianator@tavianator.com> | 2020-05-27 22:11:53 -0400 |
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committer | Tavian Barnes <tavianator@tavianator.com> | 2020-06-24 10:03:43 -0400 |

commit | 360565b36adab5f6c69e3dc09091c940a142527e (patch) | |

tree | a9231a3a079cc976322ff058a5ba9f293a320d71 | |

parent | af325f5a4ff967314fb484b77b33406d21133393 (diff) | |

download | acap-360565b36adab5f6c69e3dc09091c940a142527e.tar.xz |

Add an overview to the documentation

-rw-r--r-- | src/lib.rs | 88 |

1 files changed, 88 insertions, 0 deletions

@@ -1,6 +1,94 @@ //! As Close As Possible — [nearest neighbor search] in Rust. //! +//! # Overview +//! +//! The notion of distances between points is captured by the [Proximity] trait. Its [`distance()`] +//! method returns a [Distance], from which the actual numerical distance may be retrieved with +//! [`value()`]. These layers of abstraction allow `acap` to work with generically with different +//! distance functions over different types. +//! +//! There are no restrictions on the distances computed by a [Proximity]. For example, they don't +//! have to be symmetric, subadditive, or even positive. Implementations that do have these +//! desirable properties will additionally implement the [Metric] marker trait. This distinction +//! allows `acap` to support a wide variety of useful metric and non-metric distances. +//! +//! As a concrete example, consider `Euclidean<[i32; 2]>`. The [Euclidean] wrapper equips any type +//! that has [coordinates] with the [Euclidean distance] function as its Proximity implementation: +//! +//! use acap::distance::Proximity; +//! use acap::euclid::Euclidean; +//! +//! let a = Euclidean([3, 4]); +//! let b = Euclidean([7, 7]); +//! assert_eq!(a.distance(&b), 5); +//! +//! In this case, `distance()` doesn't return a number directly; as an optimization, it returns a +//! [EuclideanDistance] wrapper. This wrapper stores the squared value of the distance, to avoid +//! computing square roots until absolutely necessary. Still, it transparently supports comparisons +//! with numerical values: +//! +//! # use acap::distance::Proximity; +//! # use acap::euclid::Euclidean; +//! # let a = Euclidean([3, 4]); +//! # let b = Euclidean([7, 7]); +//! use acap::distance::Distance; +//! +//! let d = a.distance(&b); +//! assert!(d > 4 && d < 6); +//! assert_eq!(d, 5); +//! assert_eq!(d.value(), 5.0f32); +//! +//! For finding the nearest neighbors to a point from a set of other points, the [NearestNeighbors] +//! trait provides a uniform interface to [many different similarity search data structures]. One +//! such structure is the [vantage-point tree], available in `acap` as [VpTree]: +//! +//! # use acap::euclid::Euclidean; +//! use acap::vp::VpTree; +//! use acap::NearestNeighbors; +//! +//! let tree = VpTree::balanced(vec![ +//! Euclidean([3, 4]), +//! Euclidean([5, 12]), +//! Euclidean([8, 15]), +//! Euclidean([7, 24]), +//! ]); +//! +//! [VpTree] implements [NearestNeighbors], which has a [`nearest()`] method that returns an +//! optional [Neighbor]. The [Neighbor] struct holds the actual neighbor it found, and the distance +//! it was from the target: +//! +//! # use acap::euclid::Euclidean; +//! # use acap::vp::VpTree; +//! # use acap::NearestNeighbors; +//! # let tree = VpTree::balanced( +//! # vec![Euclidean([3, 4]), Euclidean([5, 12]), Euclidean([8, 15]), Euclidean([7, 24])] +//! # ); +//! let nearest = tree.nearest(&[7, 7]).unwrap(); +//! assert_eq!(nearest.item, &Euclidean([3, 4])); +//! assert_eq!(nearest.distance, 5); +//! +//! [NearestNeighbors] also provides the [`nearest_within()`], [`k_nearest()`], and +//! [`k_nearest_within()`] methods which find up to `k` neighbors within a possible threshold. +//! +//! It can be expensive to compute nearest neighbors exactly, especially in high dimensions. +//! For performance reasons, [NearestNeighbors] implementations are allowed to return approximate +//! results. Many implementations have a speed/accuracy tradeoff which can be tuned. Those +//! implementations which always return exact results will also implement the [ExactNeighbors] +//! marker trait. For example, a [VpTree] will be exact when the [Proximity] function is a +//! [Metric]. +//! //! [nearest neighbor search]: https://en.wikipedia.org/wiki/Nearest_neighbor_search +//! [`distance()`]: Proximity#tymethod.distance +//! [`value()`]: Distance#method.value +//! [coordinates]: Coordinates +//! [Euclidean distance]: https://en.wikipedia.org/wiki/Euclidean_distance +//! [many different similarity search data structures]: NearestNeighbors#implementors +//! [vantage-point tree]: https://en.wikipedia.org/wiki/Vantage-point_tree +//! [VpTree]: vp::VpTree +//! [`nearest()`]: NearestNeighbors#method.nearest +//! [`k_nearest()`]: NearestNeighbors#method.k_nearest +//! [`nearest_within()`]: NearestNeighbors#method.nearest_within +//! [`k_nearest_within()`]: NearestNeighbors#method.k_nearest_within pub mod coords; pub mod distance; |