//!
//! Dissimilarities for vector-vector distance
//!
//! Representing distances as pairwise dissimilarities, so to build a
//! graph of closest neighbours. This representation can be reused for
//! different implementations (initially used in this library for FastPair).
use std::cmp::{Eq, Ordering, PartialOrd};

#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};

use crate::math::num::RealNumber;

///
/// The edge of the subgraph is defined by `PairwiseDistance`.
/// The calling algorithm can store a list of distsances as
/// a list of these structures.
///
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone, Copy)]
pub struct PairwiseDistance<T: RealNumber> {
    /// index of the vector in the original `Matrix` or list
    pub node: usize,

    /// index of the closest neighbor in the original `Matrix` or same list
    pub neighbour: Option<usize>,

    /// measure of distance, according to the algorithm distance function
    /// if the distance is None, the edge has value "infinite" or max distance
    /// each algorithm has to match
    pub distance: Option<T>,
}

impl<T: RealNumber> Eq for PairwiseDistance<T> {}

impl<T: RealNumber> PartialEq for PairwiseDistance<T> {
    fn eq(&self, other: &Self) -> bool {
        self.node == other.node
            && self.neighbour == other.neighbour
            && self.distance == other.distance
    }
}

impl<T: RealNumber> PartialOrd for PairwiseDistance<T> {
    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
        self.distance.partial_cmp(&other.distance)
    }
}