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authorDaniel Mueller <deso@posteo.net>2020-01-02 08:32:06 -0800
committerDaniel Mueller <deso@posteo.net>2020-01-02 08:32:06 -0800
commitfd091b04316db9dc5fafadbd6bdbe60b127408a9 (patch)
treef202270f7ae5cedc513be03833a26148d9b5e219 /rand/rand_distr/src/dirichlet.rs
parent8161cdb26f98e65b39c603ddf7a614cc87c77a1c (diff)
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Update nitrokey crate to 0.4.0
This change finally updates the version of the nitrokey crate that we consume to 0.4.0. Along with that we update rand_core, one of its dependencies, to 0.5.1. Further more we add cfg-if in version 0.1.10 and getrandom in version 0.1.13, both of which are now new (non-development) dependencies. Import subrepo nitrokey/:nitrokey at e81057037e9b4f370b64c0a030a725bc6bdfb870 Import subrepo cfg-if/:cfg-if at 4484a6faf816ff8058088ad857b0c6bb2f4b02b2 Import subrepo getrandom/:getrandom at d661aa7e1b8cc80b47dabe3d2135b3b47d2858af Import subrepo rand/:rand at d877ed528248b52d947e0484364a4e1ae59ca502
Diffstat (limited to 'rand/rand_distr/src/dirichlet.rs')
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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 rand::Rng;
+use crate::{Distribution, Gamma, StandardNormal, Exp1, Open01};
+use crate::utils::Float;
+
+/// 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_distr::Dirichlet;
+///
+/// let dirichlet = Dirichlet::new(vec![1.0, 2.0, 3.0]).unwrap();
+/// 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<N> {
+ /// Concentration parameters (alpha)
+ alpha: Vec<N>,
+}
+
+/// Error type returned from `Dirchlet::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+ /// `alpha.len() < 2`.
+ AlphaTooShort,
+ /// `alpha <= 0.0` or `nan`.
+ AlphaTooSmall,
+ /// `size < 2`.
+ SizeTooSmall,
+}
+
+impl<N: Float> Dirichlet<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+ /// Construct a new `Dirichlet` with the given alpha parameter `alpha`.
+ ///
+ /// Requires `alpha.len() >= 2`.
+ #[inline]
+ pub fn new<V: Into<Vec<N>>>(alpha: V) -> Result<Dirichlet<N>, Error> {
+ let a = alpha.into();
+ if a.len() < 2 {
+ return Err(Error::AlphaTooShort);
+ }
+ for &ai in &a {
+ if !(ai > N::from(0.0)) {
+ return Err(Error::AlphaTooSmall);
+ }
+ }
+
+ Ok(Dirichlet { alpha: a })
+ }
+
+ /// Construct a new `Dirichlet` with the given shape parameter `alpha` and `size`.
+ ///
+ /// Requires `size >= 2`.
+ #[inline]
+ pub fn new_with_size(alpha: N, size: usize) -> Result<Dirichlet<N>, Error> {
+ if !(alpha > N::from(0.0)) {
+ return Err(Error::AlphaTooSmall);
+ }
+ if size < 2 {
+ return Err(Error::SizeTooSmall);
+ }
+ Ok(Dirichlet {
+ alpha: vec![alpha; size],
+ })
+ }
+}
+
+impl<N: Float> Distribution<Vec<N>> for Dirichlet<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+ fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Vec<N> {
+ let n = self.alpha.len();
+ let mut samples = vec![N::from(0.0); n];
+ let mut sum = N::from(0.0);
+
+ for (s, &a) in samples.iter_mut().zip(self.alpha.iter()) {
+ let g = Gamma::new(a, N::from(1.0)).unwrap();
+ *s = g.sample(rng);
+ sum += *s;
+ }
+ let invacc = N::from(1.0) / sum;
+ for s in samples.iter_mut() {
+ *s *= invacc;
+ }
+ samples
+ }
+}
+
+#[cfg(test)]
+mod test {
+ use super::Dirichlet;
+ use crate::Distribution;
+
+ #[test]
+ fn test_dirichlet() {
+ let d = Dirichlet::new(vec![1.0, 2.0, 3.0]).unwrap();
+ let mut rng = crate::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_size(alpha, size).unwrap();
+ let mut rng = crate::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_size(0.5f64, 1).unwrap();
+ }
+
+ #[test]
+ #[should_panic]
+ fn test_dirichlet_invalid_alpha() {
+ Dirichlet::new_with_size(0.0f64, 2).unwrap();
+ }
+}