use crate::{Beta, Distribution, Exp1, Open01, StandardNormal};
use core::fmt;
use num_traits::Float;
use rand::Rng;
#[derive(Clone, Copy, Debug, PartialEq)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
pub struct Pert<F>
where
F: Float,
StandardNormal: Distribution<F>,
Exp1: Distribution<F>,
Open01: Distribution<F>,
{
min: F,
range: F,
beta: Beta<F>,
}
#[derive(Clone, Copy, Debug, PartialEq, Eq)]
pub enum PertError {
RangeTooSmall,
ModeRange,
ShapeTooSmall,
}
impl fmt::Display for PertError {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.write_str(match self {
PertError::RangeTooSmall => "requirement min < max is not met in PERT distribution",
PertError::ModeRange => "mode is outside [min, max] in PERT distribution",
PertError::ShapeTooSmall => "shape < 0 or is NaN in PERT distribution",
})
}
}
#[cfg(feature = "std")]
impl std::error::Error for PertError {}
impl<F> Pert<F>
where
F: Float,
StandardNormal: Distribution<F>,
Exp1: Distribution<F>,
Open01: Distribution<F>,
{
#[allow(clippy::new_ret_no_self)]
#[inline]
pub fn new(min: F, max: F) -> PertBuilder<F> {
let shape = F::from(4.0).unwrap();
PertBuilder { min, max, shape }
}
}
#[derive(Debug)]
pub struct PertBuilder<F> {
min: F,
max: F,
shape: F,
}
impl<F> PertBuilder<F>
where
F: Float,
StandardNormal: Distribution<F>,
Exp1: Distribution<F>,
Open01: Distribution<F>,
{
#[inline]
pub fn with_shape(mut self, shape: F) -> PertBuilder<F> {
self.shape = shape;
self
}
#[inline]
pub fn with_mean(self, mean: F) -> Result<Pert<F>, PertError> {
let two = F::from(2.0).unwrap();
let mode = ((self.shape + two) * mean - self.min - self.max) / self.shape;
self.with_mode(mode)
}
#[inline]
pub fn with_mode(self, mode: F) -> Result<Pert<F>, PertError> {
if !(self.max > self.min) {
return Err(PertError::RangeTooSmall);
}
if !(mode >= self.min && self.max >= mode) {
return Err(PertError::ModeRange);
}
if !(self.shape >= F::from(0.).unwrap()) {
return Err(PertError::ShapeTooSmall);
}
let (min, max, shape) = (self.min, self.max, self.shape);
let range = max - min;
let v = F::from(1.0).unwrap() + shape * (mode - min) / range;
let w = F::from(1.0).unwrap() + shape * (max - mode) / range;
let beta = Beta::new(v, w).map_err(|_| PertError::RangeTooSmall)?;
Ok(Pert { min, range, beta })
}
}
impl<F> Distribution<F> for Pert<F>
where
F: Float,
StandardNormal: Distribution<F>,
Exp1: Distribution<F>,
Open01: Distribution<F>,
{
#[inline]
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> F {
self.beta.sample(rng) * self.range + self.min
}
}
#[cfg(test)]
mod test {
use super::*;
#[test]
fn test_pert() {
for &(min, max, mode) in &[(-1., 1., 0.), (1., 2., 1.), (5., 25., 25.)] {
let _distr = Pert::new(min, max).with_mode(mode).unwrap();
}
for &(min, max, mode) in &[(-1., 1., 2.), (-1., 1., -2.), (2., 1., 1.)] {
assert!(Pert::new(min, max).with_mode(mode).is_err());
}
}
#[test]
fn distributions_can_be_compared() {
let (min, mode, max, shape) = (1.0, 2.0, 3.0, 4.0);
let p1 = Pert::new(min, max).with_mode(mode).unwrap();
let mean = (min + shape * mode + max) / (shape + 2.0);
let p2 = Pert::new(min, max).with_mean(mean).unwrap();
assert_eq!(p1, p2);
}
#[test]
fn mode_almost_half_range() {
assert!(Pert::new(0.0f32, 0.48258883).with_mode(0.24129441).is_ok());
}
#[test]
fn almost_symmetric_about_zero() {
let distr = Pert::new(-10f32, 10f32).with_mode(f32::EPSILON);
assert!(distr.is_ok());
}
#[test]
fn almost_symmetric() {
let distr = Pert::new(0f32, 2f32).with_mode(1f32 + f32::EPSILON);
assert!(distr.is_ok());
}
}