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use rand::{Rng, distributions::{Uniform, Distribution}};
use std::iter::Iterator;
fn fill <I, T> (iter: &mut I, sample: &mut [T]) -> Option<()>
where
I: Iterator<Item=T>,
{
for element in sample.iter_mut() {
*element = match iter.next() {Some(n) => n, None => return None};
}
Some(())
}
pub fn l <R, I, T>(mut iter: I, sample: &mut [T], rng: &mut R)
where
R: Rng + ?Sized,
I: Iterator<Item=T>,
{
if let None = fill (&mut iter, sample) {
return;
}
let random_index = Uniform::from(0..sample.len());
let mut w: f64 = (rng.gen::<f64>().ln() / sample.len() as f64).exp();
let mut i = sample.len();
loop {
i += (rng.gen::<f64>().ln() / (1.0 - w).ln()).floor() as usize + 1;
match iter.nth(i) {
Some(n) => {
sample[random_index.sample(rng)] = n;
w *= (rng.gen::<f64>().ln() / sample.len() as f64).exp();
}
None => break,
}
};
}
pub fn r <R, I, T>(mut iter: I, sample: &mut [T], rng: &mut R)
where
R: Rng + ?Sized,
I: Iterator<Item=T>,
{
if let None = fill (&mut iter, sample) {
return;
}
let mut random_number;
let mut i = sample.len();
for ele in iter {
random_number = rng.gen_range(0..i);
if random_number < sample.len() {
sample[random_number] = ele;
}
i += 1;
};
}