58 lines
1.6 KiB
Rust
58 lines
1.6 KiB
Rust
use bencher::{benchmark_group, benchmark_main, Bencher};
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use rand::rngs::{StdRng, ThreadRng};
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use rand::{Rng, SeedableRng};
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use instant_distance::Builder;
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benchmark_main!(benches);
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benchmark_group!(benches, build_heuristic);
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fn build_heuristic(bench: &mut Bencher) {
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let seed = ThreadRng::default().gen::<u64>();
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let mut rng = StdRng::seed_from_u64(seed);
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let points = (0..1024)
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.into_iter()
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.map(|_| Point(rng.gen(), rng.gen()))
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.collect::<Vec<_>>();
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bench.iter(|| Builder::default().seed(seed).build(&points))
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}
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/*
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fn randomized(builder: Builder) -> (u64, usize) {
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let query = Point(rng.gen(), rng.gen());
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let mut nearest = Vec::with_capacity(256);
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for (i, p) in points.iter().enumerate() {
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nearest.push((OrderedFloat::from(query.distance(p)), i));
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if nearest.len() >= 200 {
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nearest.sort_unstable();
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nearest.truncate(100);
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}
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}
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let mut search = Search::default();
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let mut results = vec![PointId::default(); 100];
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let found = hnsw.search(&query, &mut results, &mut search);
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assert_eq!(found, 100);
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nearest.sort_unstable();
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nearest.truncate(100);
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let forced = nearest
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.iter()
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.map(|(_, i)| pids[*i])
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.collect::<HashSet<_>>();
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let found = results.into_iter().take(found).collect::<HashSet<_>>();
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(seed, forced.intersection(&found).count())
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}
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*/
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#[derive(Clone, Copy, Debug)]
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struct Point(f32, f32);
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impl instant_distance::Point for Point {
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fn distance(&self, other: &Self) -> f32 {
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// Euclidean distance metric
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((self.0 - other.0).powi(2) + (self.1 - other.1).powi(2)).sqrt()
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}
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}
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