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use std::cmp;
use float::Float;
use num_cpus;
use thread_scoped as thread;
use tuple::{Tuple, TupledDistributionsBuilder};
use univariate::resamples::Resamples;
use univariate::Sample;
pub fn bootstrap<A, T, S>(
a: &Sample<A>,
b: &Sample<A>,
nresamples: usize,
statistic: S,
) -> T::Distributions
where
A: Float,
S: Fn(&Sample<A>, &Sample<A>) -> T + Sync,
T: Tuple,
T::Distributions: Send,
T::Builder: Send,
{
let ncpus = num_cpus::get();
let n_a = a.len();
let n_b = b.len();
let mut c = Vec::with_capacity(n_a + n_b);
c.extend_from_slice(a);
c.extend_from_slice(b);
unsafe {
let c = Sample::new(&c);
if ncpus > 1 && nresamples > n_a {
let granularity = nresamples / ncpus + 1;
let statistic = &statistic;
let chunks = (0..ncpus)
.map(|i| {
let mut sub_distributions: T::Builder =
TupledDistributionsBuilder::new(granularity);
let offset = i * granularity;
thread::scoped(move || {
let end = cmp::min(offset + granularity, nresamples);
let mut resamples = Resamples::new(c);
for _ in offset..end {
let resample = resamples.next();
let a: &Sample<A> = Sample::new(&resample[..n_a]);
let b: &Sample<A> = Sample::new(&resample[n_a..]);
sub_distributions.push(statistic(a, b))
}
sub_distributions
})
})
.collect::<Vec<_>>();
let mut builder: T::Builder = TupledDistributionsBuilder::new(nresamples);
for chunk in chunks {
builder.extend(&mut (chunk.join()));
}
builder.complete()
} else {
let mut resamples = Resamples::new(c);
let mut distributions: T::Builder = TupledDistributionsBuilder::new(nresamples);
for _ in 0..nresamples {
let resample = resamples.next();
let a: &Sample<A> = Sample::new(&resample[..n_a]);
let b: &Sample<A> = Sample::new(&resample[n_a..]);
distributions.push(statistic(a, b))
}
distributions.complete()
}
}
}