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// Copyright 2018 Developers of the Rand project.
// Copyright 2013 The Rust Project Developers.
//
// 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 dirichlet distribution.
use Rng;
use distributions::Distribution;
use distributions::gamma::Gamma;
/// The dirichelet distribution `Dirichlet(alpha)`.
///
/// The Dirichlet distribution is a family of continuous multivariate
/// probability distributions parameterized by a vector alpha of positive reals.
/// It is a multivariate generalization of the beta distribution.
///
/// # Example
///
/// ```
/// use rand::prelude::*;
/// use rand::distributions::Dirichlet;
///
/// let dirichlet = Dirichlet::new(vec![1.0, 2.0, 3.0]);
/// let samples = dirichlet.sample(&mut rand::thread_rng());
/// println!("{:?} is from a Dirichlet([1.0, 2.0, 3.0]) distribution", samples);
/// ```
#[derive(Clone, Debug)]
pub struct Dirichlet {
/// Concentration parameters (alpha)
alpha: Vec<f64>,
}
impl Dirichlet {
/// Construct a new `Dirichlet` with the given alpha parameter `alpha`.
///
/// # Panics
/// - if `alpha.len() < 2`
///
#[inline]
pub fn new<V: Into<Vec<f64>>>(alpha: V) -> Dirichlet {
let a = alpha.into();
assert!(a.len() > 1);
for i in 0..a.len() {
assert!(a[i] > 0.0);
}
Dirichlet { alpha: a }
}
/// Construct a new `Dirichlet` with the given shape parameter `alpha` and `size`.
///
/// # Panics
/// - if `alpha <= 0.0`
/// - if `size < 2`
///
#[inline]
pub fn new_with_param(alpha: f64, size: usize) -> Dirichlet {
assert!(alpha > 0.0);
assert!(size > 1);
Dirichlet {
alpha: vec![alpha; size],
}
}
}
impl Distribution<Vec<f64>> for Dirichlet {
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Vec<f64> {
let n = self.alpha.len();
let mut samples = vec![0.0f64; n];
let mut sum = 0.0f64;
for i in 0..n {
let g = Gamma::new(self.alpha[i], 1.0);
samples[i] = g.sample(rng);
sum += samples[i];
}
let invacc = 1.0 / sum;
for i in 0..n {
samples[i] *= invacc;
}
samples
}
}
#[cfg(test)]
mod test {
use super::Dirichlet;
use distributions::Distribution;
#[test]
fn test_dirichlet() {
let d = Dirichlet::new(vec![1.0, 2.0, 3.0]);
let mut rng = ::test::rng(221);
let samples = d.sample(&mut rng);
let _: Vec<f64> = samples
.into_iter()
.map(|x| {
assert!(x > 0.0);
x
})
.collect();
}
#[test]
fn test_dirichlet_with_param() {
let alpha = 0.5f64;
let size = 2;
let d = Dirichlet::new_with_param(alpha, size);
let mut rng = ::test::rng(221);
let samples = d.sample(&mut rng);
let _: Vec<f64> = samples
.into_iter()
.map(|x| {
assert!(x > 0.0);
x
})
.collect();
}
#[test]
#[should_panic]
fn test_dirichlet_invalid_length() {
Dirichlet::new_with_param(0.5f64, 1);
}
#[test]
#[should_panic]
fn test_dirichlet_invalid_alpha() {
Dirichlet::new_with_param(0.0f64, 2);
}
}
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