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use num_traits::Float;
use crate::{Distribution, OpenClosed01};
use rand::Rng;
use core::fmt;
#[derive(Clone, Copy, Debug)]
pub struct Weibull<F>
where F: Float, OpenClosed01: Distribution<F>
{
inv_shape: F,
scale: F,
}
#[derive(Clone, Copy, Debug, PartialEq, Eq)]
pub enum Error {
ScaleTooSmall,
ShapeTooSmall,
}
impl fmt::Display for Error {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.write_str(match self {
Error::ScaleTooSmall => "scale is not positive in Weibull distribution",
Error::ShapeTooSmall => "shape is not positive in Weibull distribution",
})
}
}
#[cfg(feature = "std")]
impl std::error::Error for Error {}
impl<F> Weibull<F>
where F: Float, OpenClosed01: Distribution<F>
{
pub fn new(scale: F, shape: F) -> Result<Weibull<F>, Error> {
if !(scale > F::zero()) {
return Err(Error::ScaleTooSmall);
}
if !(shape > F::zero()) {
return Err(Error::ShapeTooSmall);
}
Ok(Weibull {
inv_shape: F::from(1.).unwrap() / shape,
scale,
})
}
}
impl<F> Distribution<F> for Weibull<F>
where F: Float, OpenClosed01: Distribution<F>
{
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> F {
let x: F = rng.sample(OpenClosed01);
self.scale * (-x.ln()).powf(self.inv_shape)
}
}
#[cfg(test)]
mod tests {
use super::*;
#[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.);
}
}
#[test]
fn value_stability() {
fn test_samples<F: Float + core::fmt::Debug, D: Distribution<F>>(
distr: D, zero: F, expected: &[F],
) {
let mut rng = crate::test::rng(213);
let mut buf = [zero; 4];
for x in &mut buf {
*x = rng.sample(&distr);
}
assert_eq!(buf, expected);
}
test_samples(Weibull::new(1.0, 1.0).unwrap(), 0f32, &[
0.041495778,
0.7531094,
1.4189332,
0.38386202,
]);
test_samples(Weibull::new(2.0, 0.5).unwrap(), 0f64, &[
1.1343478702739669,
0.29470010050655226,
0.7556151370284702,
7.877212340241561,
]);
}
}