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Implement triangular distribution #575
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// 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. | ||
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use Rng; | ||
use distributions::{Distribution, Standard}; | ||
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/// 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, | ||
} | ||
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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 } | ||
} | ||
} | ||
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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() | ||
} | ||
} | ||
} | ||
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#[cfg(test)] | ||
mod test { | ||
use distributions::Distribution; | ||
use super::Triangular; | ||
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#[test] | ||
fn test_new() { | ||
for &(min, max, mode) in &[ | ||
(-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); | ||
} | ||
} | ||
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#[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); | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. This also doesn't do much. It could at least test the mean. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. 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 |
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} | ||
} | ||
} |
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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?There was a problem hiding this comment.
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You mean this test specifically or the distribution in general?