use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};

// to run this bench you have to change the declaraion in mod.rs ---> pub mod fastpair;
use smartcore::algorithm::neighbour::fastpair::FastPair;
use smartcore::linalg::naive::dense_matrix::*;
use std::time::Duration;

fn closest_pair_bench(n: usize, m: usize) -> () {
    let x = DenseMatrix::<f64>::rand(n, m);
    let fastpair = FastPair::new(&x);
    let result = fastpair.unwrap();

    result.closest_pair();
}

fn closest_pair_brute_bench(n: usize, m: usize) -> () {
    let x = DenseMatrix::<f64>::rand(n, m);
    let fastpair = FastPair::new(&x);
    let result = fastpair.unwrap();

    result.closest_pair_brute();
}

fn bench_fastpair(c: &mut Criterion) {
    let mut group = c.benchmark_group("FastPair");

    // with full samples size (100) the test will take too long
    group.significance_level(0.1).sample_size(30);
    // increase from default 5.0 secs
    group.measurement_time(Duration::from_secs(60));

    for n_samples in [100_usize, 1000_usize].iter() {
        for n_features in [10_usize, 100_usize, 1000_usize].iter() {
            group.bench_with_input(
                BenchmarkId::from_parameter(format!(
                    "fastpair --- n_samples: {}, n_features: {}",
                    n_samples, n_features
                )),
                n_samples,
                |b, _| b.iter(|| closest_pair_bench(*n_samples, *n_features)),
            );
            group.bench_with_input(
                BenchmarkId::from_parameter(format!(
                    "brute --- n_samples: {}, n_features: {}",
                    n_samples, n_features
                )),
                n_samples,
                |b, _| b.iter(|| closest_pair_brute_bench(*n_samples, *n_features)),
            );
        }
    }
    group.finish();
}

criterion_group!(benches, bench_fastpair);
criterion_main!(benches);