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use rand::Rng;
use crate::{Distribution, OpenClosed01};
use crate::utils::Float;
#[derive(Clone, Copy, Debug)]
pub struct Weibull<N> {
inv_shape: N,
scale: N,
}
#[derive(Clone, Copy, Debug, PartialEq, Eq)]
pub enum Error {
ScaleTooSmall,
ShapeTooSmall,
}
impl<N: Float> Weibull<N>
where OpenClosed01: Distribution<N>
{
pub fn new(scale: N, shape: N) -> Result<Weibull<N>, Error> {
if !(scale > N::from(0.0)) {
return Err(Error::ScaleTooSmall);
}
if !(shape > N::from(0.0)) {
return Err(Error::ShapeTooSmall);
}
Ok(Weibull { inv_shape: N::from(1.)/shape, scale })
}
}
impl<N: Float> Distribution<N> for Weibull<N>
where OpenClosed01: Distribution<N>
{
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
let x: N = rng.sample(OpenClosed01);
self.scale * (-x.ln()).powf(self.inv_shape)
}
}
#[cfg(test)]
mod tests {
use crate::Distribution;
use super::Weibull;
#[test]
#[should_panic]
fn invalid() {
Weibull::new(0., 0.).unwrap();
}
#[test]
fn sample() {
let scale = 1.0;
let shape = 2.0;
let d = Weibull::new(scale, shape).unwrap();
let mut rng = crate::test::rng(1);
for _ in 0..1000 {
let r = d.sample(&mut rng);
assert!(r >= 0.);
}
}
}