hannoy 0.1.3

HNSW Approximate Nearest Neighbors in Rust, based on LMDB and optimized for memory usage
Documentation 
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use std::fmt;

use bytemuck::{Pod, Zeroable};

use crate::distance::Distance;
use crate::node::Item;
use crate::spaces::simple::dot_product;
use crate::unaligned_vector::UnalignedVector;

/// A taxicab geometry or a Manhattan geometry is a geometry whose usual distance function
/// or metric of Euclidean geometry is replaced by a new metric in which the distance between
/// two points is the sum of the absolute differences of their Cartesian coordinates.
#[derive(Debug, Clone)]
pub enum Manhattan {}

/// The header of Manhattan item nodes.
#[repr(C)]
#[derive(Pod, Zeroable, Clone, Copy)]
pub struct NodeHeaderManhattan {
    /// An extra constant term to determine the offset of the plane
    bias: f32,
}
impl fmt::Debug for NodeHeaderManhattan {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        f.debug_struct("NodeHeaderManhattan").field("bias", &format!("{:.4}", self.bias)).finish()
    }
}

impl Distance for Manhattan {
    type Header = NodeHeaderManhattan;
    type VectorCodec = f32;

    fn name() -> &'static str {
        "manhattan"
    }

    fn new_header(_vector: &UnalignedVector<Self::VectorCodec>) -> Self::Header {
        NodeHeaderManhattan { bias: 0.0 }
    }

    fn distance(p: &Item<Self>, q: &Item<Self>) -> f32 {
        p.vector.iter().zip(q.vector.iter()).map(|(p, q)| (p - q).abs()).sum()
    }

    fn norm_no_header(v: &UnalignedVector<Self::VectorCodec>) -> f32 {
        dot_product(v, v).sqrt()
    }
}