Disambiguate distances. Implement Fastpair. (#220)
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@@ -24,9 +24,15 @@ pub mod manhattan;
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/// A generalization of both the Euclidean distance and the Manhattan distance.
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pub mod minkowski;
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use std::cmp::{Eq, Ordering, PartialOrd};
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use crate::linalg::basic::arrays::Array2;
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use crate::linalg::traits::lu::LUDecomposable;
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use crate::numbers::basenum::Number;
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use crate::numbers::realnum::RealNumber;
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#[cfg(feature = "serde")]
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use serde::{Deserialize, Serialize};
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/// Distance metric, a function that calculates distance between two points
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pub trait Distance<T>: Clone {
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@@ -66,3 +72,45 @@ impl Distances {
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mahalanobis::Mahalanobis::new(data)
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}
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}
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///
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/// ### Pairwise dissimilarities.
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///
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/// Representing distances as pairwise dissimilarities, so to build a
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/// graph of closest neighbours. This representation can be reused for
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/// different implementations
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/// (initially used in this library for [FastPair](algorithm/neighbour/fastpair)).
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/// The edge of the subgraph is defined by `PairwiseDistance`.
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/// The calling algorithm can store a list of distances as
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/// a list of these structures.
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///
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, Clone, Copy)]
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pub struct PairwiseDistance<T: RealNumber> {
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/// index of the vector in the original `Matrix` or list
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pub node: usize,
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/// index of the closest neighbor in the original `Matrix` or same list
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pub neighbour: Option<usize>,
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/// measure of distance, according to the algorithm distance function
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/// if the distance is None, the edge has value "infinite" or max distance
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/// each algorithm has to match
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pub distance: Option<T>,
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}
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impl<T: RealNumber> Eq for PairwiseDistance<T> {}
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impl<T: RealNumber> PartialEq for PairwiseDistance<T> {
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fn eq(&self, other: &Self) -> bool {
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self.node == other.node
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&& self.neighbour == other.neighbour
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&& self.distance == other.distance
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}
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}
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impl<T: RealNumber> PartialOrd for PairwiseDistance<T> {
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fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
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self.distance.partial_cmp(&other.distance)
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}
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}
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