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Implement triangular distribution #575

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Aug 4, 2018
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5 changes: 5 additions & 0 deletions src/distributions/mod.rs
Original file line number Diff line number Diff line change
Expand Up @@ -98,6 +98,8 @@
//! - [`ChiSquared`] distribution
//! - [`StudentT`] distribution
//! - [`FisherF`] distribution
//! - Triangular distribution:
//! - [`Triangular`] distribution
//! - Multivariate probability distributions
//! - [`Dirichlet`] distribution
//! - [`UnitSphereSurface`] distribution
Expand Down Expand Up @@ -168,6 +170,7 @@
//! [`Standard`]: struct.Standard.html
//! [`StandardNormal`]: struct.StandardNormal.html
//! [`StudentT`]: struct.StudentT.html
//! [`Triangular`]: struct.Triangular.html
//! [`Uniform`]: struct.Uniform.html
//! [`Uniform::new`]: struct.Uniform.html#method.new
//! [`Uniform::new_inclusive`]: struct.Uniform.html#method.new_inclusive
Expand All @@ -192,6 +195,7 @@ pub use self::bernoulli::Bernoulli;
#[cfg(feature="std")] pub use self::binomial::Binomial;
#[cfg(feature="std")] pub use self::cauchy::Cauchy;
#[cfg(feature="std")] pub use self::dirichlet::Dirichlet;
#[cfg(feature="std")] pub use self::triangular::Triangular;

pub mod uniform;
mod bernoulli;
Expand All @@ -206,6 +210,7 @@ mod bernoulli;
#[cfg(feature="std")] mod binomial;
#[cfg(feature="std")] mod cauchy;
#[cfg(feature="std")] mod dirichlet;
#[cfg(feature="std")] mod triangular;

mod float;
mod integer;
Expand Down
88 changes: 88 additions & 0 deletions src/distributions/triangular.rs
Original file line number Diff line number Diff line change
@@ -0,0 +1,88 @@
// Copyright 2018 The Rust Project Developers. See the COPYRIGHT
// file at the top-level directory of this distribution and at
// https://rust-lang.org/COPYRIGHT.
//
// 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 Rng;
use distributions::{Distribution, Standard};

/// The triangular distribution.
///
/// # Example
///
/// ```rust
/// use rand::distributions::{Triangular, Distribution};
///
/// let d = Triangular::new(0., 5., 2.5);
/// let v = d.sample(&mut rand::thread_rng());
/// println!("{} is from a triangular distribution", v);
/// ```
#[derive(Clone, Copy, Debug)]
pub struct Triangular {
min: f64,
max: f64,
mode: f64,
}

impl Triangular {
/// Construct a new `Triangular` with minimum `min`, maximum `max` and mode
/// `mode`.
///
/// # Panics
///
/// If `max < mode`, `mode < max` or `max == min`.
///
#[inline]
pub fn new(min: f64, max: f64, mode: f64) -> Triangular {
assert!(max >= mode);
assert!(mode >= min);
assert!(max != min);
Triangular { min, max, mode }
}
}

impl Distribution<f64> for Triangular {
#[inline]
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
let f: f64 = rng.sample(Standard);
let diff_mode_min = self.mode - self.min;
let diff_max_min = self.max - self.min;
if f * diff_max_min < diff_mode_min {
self.min + (f * diff_max_min * diff_mode_min).sqrt()
} else {
self.max - ((1. - f) * diff_max_min * (self.max - self.mode)).sqrt()
}
}
}

#[cfg(test)]
mod test {
use distributions::Distribution;
use super::Triangular;

#[test]
fn test_new() {
for &(min, max, mode) in &[
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This test does very little...

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It just tests that the asserts are implemented correctly. I ported the test from statrs.

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So you're just copying from statrs. I wondered. Why?

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You mean this test specifically or the distribution in general?

(-1., 1., 0.), (1., 2., 1.), (5., 25., 25.), (1e-5, 1e5, 1e-3),
(0., 1., 0.9), (-4., -0.5, -2.), (-13.039, 8.41, 1.17),
] {
println!("{} {} {}", min, max, mode);
let _ = Triangular::new(min, max, mode);
}
}

#[test]
fn test_sample() {
let norm = Triangular::new(0., 1., 0.5);
let mut rng = ::test::rng(1);
for _ in 0..1000 {
norm.sample(&mut rng);
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This also doesn't do much. It could at least test the mean.

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Fair enough. This is the status quo of most distribution tests in Rand though.

In principle we should use histograms and test against the PDF. (I plan to work on that in the midterm, probably after implementing PDFs for #290 and by generalizing tests/uniformity.rs.)

}
}
}