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//! Benchmark for various NearestNeighbors implementations.
use acap::euclid::Euclidean;
use acap::exhaustive::ExhaustiveSearch;
use acap::kd::{FlatKdTree, KdTree};
use acap::vp::{FlatVpTree, VpTree};
use acap::NearestNeighbors;
use criterion::{black_box, criterion_group, criterion_main, BatchSize, Criterion};
use std::iter::FromIterator;
type Point = Euclidean<[f32; 3]>;
/// Generates a spiral used as the benchmark data set.
fn spiral() -> Vec<Point> {
let mut points = Vec::new();
let size = 1000;
let turns = 10.0;
for i in 0..size {
let y = 2.0 * (i as f32) / (size as f32) - 1.0;
let m = (1.0 - y * y).sqrt();
let theta = turns * y * std::f32::consts::PI;
let (sin, cos) = theta.sin_cos();
let x = m * cos * cos;
let z = m * sin * cos;
points.push(Euclidean([x, y, z]));
}
points
}
fn bench_creation(c: &mut Criterion) {
let points = black_box(spiral());
let mut group = c.benchmark_group("Creation");
macro_rules! bench {
($type:ident) => {
group.bench_function(stringify!($type), |b| b.iter_batched(
|| points.clone(),
|points| $type::from_iter(points),
BatchSize::SmallInput,
));
};
}
bench!(ExhaustiveSearch);
bench!(VpTree);
bench!(FlatVpTree);
bench!(KdTree);
bench!(FlatKdTree);
group.finish();
}
fn bench_nearest_neighbors(c: &mut Criterion) {
let points = black_box(spiral());
let target = black_box(Euclidean([0.0, 0.0, 0.0]));
macro_rules! bench {
($type:ident) => {
let mut group = c.benchmark_group(stringify!($type));
let index = $type::from_iter(points.clone());
group.bench_function("nearest", |b| b.iter(
|| index.nearest(&target)
));
group.bench_function("nearest_within", |b| b.iter(
|| index.nearest_within(&target, 0.1)
));
group.bench_function("k_nearest", |b| b.iter(
|| index.k_nearest(&target, 3)
));
group.bench_function("k_nearest_within", |b| b.iter(
|| index.k_nearest_within(&target, 3, 0.1)
));
group.bench_function("merge_k_nearest", |b| b.iter_batched(
|| Vec::with_capacity(3),
|mut n| {
index.merge_k_nearest(&target, 3, &mut n);
n
},
BatchSize::SmallInput,
));
group.bench_function("merge_k_nearest_within", |b| b.iter_batched(
|| Vec::with_capacity(3),
|mut n| {
index.merge_k_nearest_within(&target, 3, 0.1, &mut n);
n
},
BatchSize::SmallInput,
));
group.finish();
};
}
bench!(ExhaustiveSearch);
bench!(VpTree);
bench!(FlatVpTree);
bench!(KdTree);
bench!(FlatKdTree);
}
criterion_group!(benches, bench_creation, bench_nearest_neighbors);
criterion_main!(benches);
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