35 releases (17 breaking)
0.18.0 | Aug 14, 2021 |
---|---|
0.17.0 | Jul 18, 2021 |
0.16.0 | Jul 18, 2021 |
0.10.3 | Mar 5, 2020 |
0.6.0 | Nov 20, 2018 |
#572 in Data structures
21,319 downloads per month
Used in 20 crates
(8 directly)
18KB
155 lines
space
A library providing abstractions for spatial datastructures and search
If you use a kNN datastructure library and would like to have the Knn
trait implemented on its types natively, please raise an issue on that library.
Similarly, crates which define datapoints with specific distance metrics, and
not general linear algebra crates, can implement the MetricPoint
trait.
See the bitarray crate for an implementation
of MetricPoint
using hamming distance (with optional, though unstable, 512-bit
SIMD support, and always-on 64-bit popcnt instruction support).
Usage
use space::Metric;
struct Hamming;
impl Metric<u8> for Hamming {
type Unit = u8;
fn distance(&self, &a: &u8, &b: &u8) -> Self::Unit {
(a ^ b).count_ones() as u8
}
}
use space::{Knn, KnnFromBatch, LinearKnn, Metric, Neighbor};
#[derive(Default)]
struct Hamming;
impl Metric<u8> for Hamming {
type Unit = u8;
fn distance(&self, &a: &u8, &b: &u8) -> Self::Unit {
(a ^ b).count_ones() as u8
}
}
let data = vec![
(0b1010_1010, 12),
(0b1111_1111, 13),
(0b0000_0000, 14),
(0b1111_0000, 16),
(0b0000_1111, 10),
];
let search: LinearKnn<Hamming, _> = KnnFromBatch::from_batch(data.iter());
assert_eq!(
&search.knn(&0b0101_0000, 3),
&[
(
Neighbor {
index: 2,
distance: 2
},
&data[2].0,
&data[2].1
),
(
Neighbor {
index: 3,
distance: 2
},
&data[3].0,
&data[3].1
),
(
Neighbor {
index: 0,
distance: 6
},
&data[0].0,
&data[0].1
)
]
);
Benchmarks
To run the benchmarks, use the following command:
cargo bench --all-features
If you do not pass --all-features
, the benchmark wont run. Due to this issue, the SIMD feature must be enabled. Cargo offers no way to automatically bring the SIMD feature in for the benchmark, and thus it must be passed at the command line.
Dependencies
~100–325KB