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pub mod chi_squared;
pub mod params;
pub use chi_squared::*;
pub use params::*;
use crate::{DependentJoint, Distribution, IndependentJoint, RandomVariable};
use crate::{DistributionError, SampleableDistribution};
use rand::prelude::*;
use rand_distr::Gamma as RandGamma;
use std::{ops::BitAnd, ops::Mul};
#[derive(Clone, Debug)]
pub struct Gamma;
#[derive(thiserror::Error, Debug)]
pub enum GammaError {
#[error("'shape' must be positive")]
ShapeMustBePositive,
#[error("'scale' must be positive")]
ScaleMustBePositive,
}
impl Distribution for Gamma {
type Value = f64;
type Condition = GammaParams;
fn p_kernel(&self, x: &Self::Value, theta: &Self::Condition) -> Result<f64, DistributionError> {
let shape = theta.shape();
let scale = theta.scale();
Ok(x.powf(shape - 1.0) * (-x / scale).exp())
}
}
impl<Rhs, TRhs> Mul<Rhs> for Gamma
where
Rhs: Distribution<Value = TRhs, Condition = GammaParams>,
TRhs: RandomVariable,
{
type Output = IndependentJoint<Self, Rhs, f64, TRhs, GammaParams>;
fn mul(self, rhs: Rhs) -> Self::Output {
IndependentJoint::new(self, rhs)
}
}
impl<Rhs, URhs> BitAnd<Rhs> for Gamma
where
Rhs: Distribution<Value = GammaParams, Condition = URhs>,
URhs: RandomVariable,
{
type Output = DependentJoint<Self, Rhs, f64, GammaParams, URhs>;
fn bitand(self, rhs: Rhs) -> Self::Output {
DependentJoint::new(self, rhs)
}
}
impl SampleableDistribution for Gamma {
fn sample(
&self,
theta: &Self::Condition,
rng: &mut dyn RngCore,
) -> Result<Self::Value, DistributionError> {
let shape = theta.shape();
let scale = theta.scale();
let gamma = match RandGamma::new(shape, scale) {
Ok(v) => Ok(v),
Err(e) => Err(DistributionError::Others(e.into())),
}?;
Ok(rng.sample(gamma))
}
}
#[cfg(test)]
mod tests {
#[test]
fn it_works() {
assert_eq!(2 + 2, 4);
}
}