blob: 040cd05cfed911cc1aab6aa55ab6afb5cfffd6e9 [file] [log] [blame]
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.
//! The PERT distribution.
use rand::Rng;
use crate::{Distribution, Beta, StandardNormal, Exp1, Open01};
use crate::utils::Float;
/// The PERT distribution.
///
/// Similar to the [`Triangular`] distribution, the PERT distribution is
/// parameterised by a range and a mode within that range. Unlike the
/// [`Triangular`] distribution, the probability density function of the PERT
/// distribution is smooth, with a configurable weighting around the mode.
///
/// # Example
///
/// ```rust
/// use rand_distr::{Pert, Distribution};
///
/// let d = Pert::new(0., 5., 2.5).unwrap();
/// let v = d.sample(&mut rand::thread_rng());
/// println!("{} is from a PERT distribution", v);
/// ```
///
/// [`Triangular`]: crate::Triangular
#[derive(Clone, Copy, Debug)]
pub struct Pert<N> {
min: N,
range: N,
beta: Beta<N>,
}
/// Error type returned from [`Pert`] constructors.
#[derive(Clone, Copy, Debug, PartialEq, Eq)]
pub enum PertError {
/// `max < min` or `min` or `max` is NaN.
RangeTooSmall,
/// `mode < min` or `mode > max` or `mode` is NaN.
ModeRange,
/// `shape < 0` or `shape` is NaN
ShapeTooSmall,
}
impl<N: Float> Pert<N>
where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
{
/// Set up the PERT distribution with defined `min`, `max` and `mode`.
///
/// This is equivalent to calling `Pert::new_shape` with `shape == 4.0`.
#[inline]
pub fn new(min: N, max: N, mode: N) -> Result<Pert<N>, PertError> {
Pert::new_with_shape(min, max, mode, N::from(4.))
}
/// Set up the PERT distribution with defined `min`, `max`, `mode` and
/// `shape`.
pub fn new_with_shape(min: N, max: N, mode: N, shape: N) -> Result<Pert<N>, PertError> {
if !(max > min) {
return Err(PertError::RangeTooSmall);
}
if !(mode >= min && max >= mode) {
return Err(PertError::ModeRange);
}
if !(shape >= N::from(0.)) {
return Err(PertError::ShapeTooSmall);
}
let range = max - min;
let mu = (min + max + shape * mode) / (shape + N::from(2.));
let v = if mu == mode {
shape * N::from(0.5) + N::from(1.)
} else {
(mu - min) * (N::from(2.) * mode - min - max)
/ ((mode - mu) * (max - min))
};
let w = v * (max - mu) / (mu - min);
let beta = Beta::new(v, w).map_err(|_| PertError::RangeTooSmall)?;
Ok(Pert{ min, range, beta })
}
}
impl<N: Float> Distribution<N> for Pert<N>
where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
{
#[inline]
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
self.beta.sample(rng) * self.range + self.min
}
}
#[cfg(test)]
mod test {
use std::f64;
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, mode).unwrap();
// TODO: test correctness
}
for &(min, max, mode) in &[
(-1., 1., 2.),
(-1., 1., -2.),
(2., 1., 1.),
] {
assert!(Pert::new(min, max, mode).is_err());
}
}
#[test]
fn value_stability() {
let rng = crate::test::rng(860);
let distr = Pert::new(2., 10., 3.).unwrap(); // mean = 4, var = 12/7
let seq = distr.sample_iter(rng).take(5).collect::<Vec<f64>>();
println!("seq: {:?}", seq);
let expected = vec![4.631484136029422, 3.307201472321789,
3.29995019556348, 3.66835483991721, 3.514246139933899];
assert!(seq == expected);
}
}