aboutsummaryrefslogtreecommitdiff
path: root/rand/rand_distr/src/triangular.rs
diff options
context:
space:
mode:
Diffstat (limited to 'rand/rand_distr/src/triangular.rs')
-rw-r--r--rand/rand_distr/src/triangular.rs125
1 files changed, 0 insertions, 125 deletions
diff --git a/rand/rand_distr/src/triangular.rs b/rand/rand_distr/src/triangular.rs
deleted file mode 100644
index dd0bbfb..0000000
--- a/rand/rand_distr/src/triangular.rs
+++ /dev/null
@@ -1,125 +0,0 @@
-// 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 triangular distribution.
-
-use rand::Rng;
-use crate::{Distribution, Standard};
-use crate::utils::Float;
-
-/// The triangular distribution.
-///
-/// A continuous probability distribution parameterised by a range, and a mode
-/// (most likely value) within that range.
-///
-/// The probability density function is triangular. For a similar distribution
-/// with a smooth PDF, see the [`Pert`] distribution.
-///
-/// # Example
-///
-/// ```rust
-/// use rand_distr::{Triangular, Distribution};
-///
-/// let d = Triangular::new(0., 5., 2.5).unwrap();
-/// let v = d.sample(&mut rand::thread_rng());
-/// println!("{} is from a triangular distribution", v);
-/// ```
-///
-/// [`Pert`]: crate::Pert
-#[derive(Clone, Copy, Debug)]
-pub struct Triangular<N> {
- min: N,
- max: N,
- mode: N,
-}
-
-/// Error type returned from [`Triangular::new`].
-#[derive(Clone, Copy, Debug, PartialEq, Eq)]
-pub enum TriangularError {
- /// `max < min` or `min` or `max` is NaN.
- RangeTooSmall,
- /// `mode < min` or `mode > max` or `mode` is NaN.
- ModeRange,
-}
-
-impl<N: Float> Triangular<N>
-where Standard: Distribution<N>
-{
- /// Set up the Triangular distribution with defined `min`, `max` and `mode`.
- #[inline]
- pub fn new(min: N, max: N, mode: N) -> Result<Triangular<N>, TriangularError> {
- if !(max >= min) {
- return Err(TriangularError::RangeTooSmall);
- }
- if !(mode >= min && max >= mode) {
- return Err(TriangularError::ModeRange);
- }
- Ok(Triangular { min, max, mode })
- }
-}
-
-impl<N: Float> Distribution<N> for Triangular<N>
-where Standard: Distribution<N>
-{
- #[inline]
- fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
- let f: N = rng.sample(Standard);
- let diff_mode_min = self.mode - self.min;
- let range = self.max - self.min;
- let f_range = f * range;
- if f_range < diff_mode_min {
- self.min + (f_range * diff_mode_min).sqrt()
- } else {
- self.max - ((range - f_range) * (self.max - self.mode)).sqrt()
- }
- }
-}
-
-#[cfg(test)]
-mod test {
- use std::f64;
- use rand::{Rng, rngs::mock};
- use super::*;
-
- #[test]
- fn test_triangular() {
- let mut half_rng = mock::StepRng::new(0x8000_0000_0000_0000, 0);
- assert_eq!(half_rng.gen::<f64>(), 0.5);
- for &(min, max, mode, median) in &[
- (-1., 1., 0., 0.),
- (1., 2., 1., 2. - 0.5f64.sqrt()),
- (5., 25., 25., 5. + 200f64.sqrt()),
- (1e-5, 1e5, 1e-3, 1e5 - 4999999949.5f64.sqrt()),
- (0., 1., 0.9, 0.45f64.sqrt()),
- (-4., -0.5, -2., -4.0 + 3.5f64.sqrt()),
- ] {
- println!("{} {} {} {}", min, max, mode, median);
- let distr = Triangular::new(min, max, mode).unwrap();
- // Test correct value at median:
- assert_eq!(distr.sample(&mut half_rng), median);
- }
-
- for &(min, max, mode) in &[
- (-1., 1., 2.),
- (-1., 1., -2.),
- (2., 1., 1.),
- ] {
- assert!(Triangular::new(min, max, mode).is_err());
- }
- }
-
- #[test]
- fn value_stability() {
- let rng = crate::test::rng(860);
- let distr = Triangular::new(2., 10., 3.).unwrap();
- let seq = distr.sample_iter(rng).take(5).collect::<Vec<f64>>();
- println!("seq: {:?}", seq);
- let expected = vec![5.74373257511361, 7.890059162791258,
- 4.7256280652553455, 2.9474808121184077, 3.058301946314053];
- assert!(seq == expected);
- }
-}