| use crate::{Distribution, Standard, StandardNormal}; |
| use num_traits::Float; |
| use rand::Rng; |
| |
| /// Error type returned from `InverseGaussian::new` |
| #[derive(Debug, PartialEq)] |
| pub enum Error { |
| /// `mean <= 0` or `nan`. |
| MeanNegativeOrNull, |
| /// `shape <= 0` or `nan`. |
| ShapeNegativeOrNull, |
| } |
| |
| /// The [inverse Gaussian distribution](https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution) |
| #[derive(Debug)] |
| pub struct InverseGaussian<F> |
| where |
| F: Float, |
| StandardNormal: Distribution<F>, |
| Standard: Distribution<F>, |
| { |
| mean: F, |
| shape: F, |
| } |
| |
| impl<F> InverseGaussian<F> |
| where |
| F: Float, |
| StandardNormal: Distribution<F>, |
| Standard: Distribution<F>, |
| { |
| /// Construct a new `InverseGaussian` distribution with the given mean and |
| /// shape. |
| pub fn new(mean: F, shape: F) -> Result<InverseGaussian<F>, Error> { |
| let zero = F::zero(); |
| if !(mean > zero) { |
| return Err(Error::MeanNegativeOrNull); |
| } |
| |
| if !(shape > zero) { |
| return Err(Error::ShapeNegativeOrNull); |
| } |
| |
| Ok(Self { mean, shape }) |
| } |
| } |
| |
| impl<F> Distribution<F> for InverseGaussian<F> |
| where |
| F: Float, |
| StandardNormal: Distribution<F>, |
| Standard: Distribution<F>, |
| { |
| fn sample<R>(&self, rng: &mut R) -> F |
| where R: Rng + ?Sized { |
| let mu = self.mean; |
| let l = self.shape; |
| |
| let v: F = rng.sample(StandardNormal); |
| let y = mu * v * v; |
| |
| let mu_2l = mu / (F::from(2.).unwrap() * l); |
| |
| let x = mu + mu_2l * (y - (F::from(4.).unwrap() * l * y + y * y).sqrt()); |
| |
| let u: F = rng.gen(); |
| |
| if u <= mu / (mu + x) { |
| return x; |
| } |
| |
| mu * mu / x |
| } |
| } |
| |
| #[cfg(test)] |
| mod tests { |
| use super::*; |
| |
| #[test] |
| fn test_inverse_gaussian() { |
| let inv_gauss = InverseGaussian::new(1.0, 1.0).unwrap(); |
| let mut rng = crate::test::rng(210); |
| for _ in 0..1000 { |
| inv_gauss.sample(&mut rng); |
| } |
| } |
| |
| #[test] |
| fn test_inverse_gaussian_invalid_param() { |
| assert!(InverseGaussian::new(-1.0, 1.0).is_err()); |
| assert!(InverseGaussian::new(-1.0, -1.0).is_err()); |
| assert!(InverseGaussian::new(1.0, -1.0).is_err()); |
| assert!(InverseGaussian::new(1.0, 1.0).is_ok()); |
| } |
| } |