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//! Univariate analysis
mod bootstrap;
mod percentiles;
mod resamples;
mod sample;
pub mod kde;
pub mod mixed;
pub mod outliers;
use crate::stats::float::Float;
use crate::stats::tuple::{Tuple, TupledDistributionsBuilder};
use rayon::prelude::*;
use std::cmp;
use self::resamples::Resamples;
pub use self::percentiles::Percentiles;
pub use self::sample::Sample;
/// Performs a two-sample bootstrap
///
/// - Multithreaded
/// - Time: `O(nresamples)`
/// - Memory: `O(nresamples)`
#[cfg_attr(feature = "cargo-clippy", allow(clippy::cast_lossless))]
pub fn bootstrap<A, B, T, S>(
a: &Sample<A>,
b: &Sample<B>,
nresamples: usize,
statistic: S,
) -> T::Distributions
where
A: Float,
B: Float,
S: Fn(&Sample<A>, &Sample<B>) -> T + Sync,
T: Tuple + Send,
T::Distributions: Send,
T::Builder: Send,
{
let nresamples_sqrt = (nresamples as f64).sqrt().ceil() as usize;
let per_chunk = (nresamples + nresamples_sqrt - 1) / nresamples_sqrt;
(0..nresamples_sqrt)
.into_par_iter()
.map_init(
|| (Resamples::new(a), Resamples::new(b)),
|(a_resamples, b_resamples), i| {
let start = i * per_chunk;
let end = cmp::min((i + 1) * per_chunk, nresamples);
let a_resample = a_resamples.next();
let mut sub_distributions: T::Builder =
TupledDistributionsBuilder::new(end - start);
for _ in start..end {
let b_resample = b_resamples.next();
sub_distributions.push(statistic(a_resample, b_resample));
}
sub_distributions
},
)
.reduce(
|| T::Builder::new(0),
|mut a, mut b| {
a.extend(&mut b);
a
},
)
.complete()
}
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