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| </pre><pre class="rust"><code><span class="comment">// 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. |
| |
| </span><span class="doccomment">//! The dirichlet distribution. |
| </span><span class="attribute">#![cfg(feature = <span class="string">"alloc"</span>)] |
| </span><span class="kw">use </span>num_traits::Float; |
| <span class="kw">use crate</span>::{Distribution, Exp1, Gamma, Open01, StandardNormal}; |
| <span class="kw">use </span>rand::Rng; |
| <span class="kw">use </span>core::fmt; |
| <span class="kw">use </span>alloc::{boxed::Box, vec, vec::Vec}; |
| |
| <span class="doccomment">/// The Dirichlet 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(&[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); |
| /// ``` |
| </span><span class="attribute">#[cfg_attr(doc_cfg, doc(cfg(feature = <span class="string">"alloc"</span>)))] |
| #[derive(Clone, Debug)] |
| #[cfg_attr(feature = <span class="string">"serde1"</span>, derive(serde::Serialize, serde::Deserialize))] |
| </span><span class="kw">pub struct </span>Dirichlet<F> |
| <span class="kw">where |
| </span>F: Float, |
| StandardNormal: Distribution<F>, |
| Exp1: Distribution<F>, |
| Open01: Distribution<F>, |
| { |
| <span class="doccomment">/// Concentration parameters (alpha) |
| </span>alpha: Box<[F]>, |
| } |
| |
| <span class="doccomment">/// Error type returned from `Dirchlet::new`. |
| </span><span class="attribute">#[cfg_attr(doc_cfg, doc(cfg(feature = <span class="string">"alloc"</span>)))] |
| #[derive(Clone, Copy, Debug, PartialEq, Eq)] |
| </span><span class="kw">pub enum </span>Error { |
| <span class="doccomment">/// `alpha.len() < 2`. |
| </span>AlphaTooShort, |
| <span class="doccomment">/// `alpha <= 0.0` or `nan`. |
| </span>AlphaTooSmall, |
| <span class="doccomment">/// `size < 2`. |
| </span>SizeTooSmall, |
| } |
| |
| <span class="kw">impl </span>fmt::Display <span class="kw">for </span>Error { |
| <span class="kw">fn </span>fmt(<span class="kw-2">&</span><span class="self">self</span>, f: <span class="kw-2">&mut </span>fmt::Formatter<<span class="lifetime">'_</span>>) -> fmt::Result { |
| f.write_str(<span class="kw">match </span><span class="self">self </span>{ |
| Error::AlphaTooShort | Error::SizeTooSmall => { |
| <span class="string">"less than 2 dimensions in Dirichlet distribution" |
| </span>} |
| Error::AlphaTooSmall => <span class="string">"alpha is not positive in Dirichlet distribution"</span>, |
| }) |
| } |
| } |
| |
| <span class="attribute">#[cfg(feature = <span class="string">"std"</span>)] |
| #[cfg_attr(doc_cfg, doc(cfg(feature = <span class="string">"std"</span>)))] |
| </span><span class="kw">impl </span>std::error::Error <span class="kw">for </span>Error {} |
| |
| <span class="kw">impl</span><F> Dirichlet<F> |
| <span class="kw">where |
| </span>F: Float, |
| StandardNormal: Distribution<F>, |
| Exp1: Distribution<F>, |
| Open01: Distribution<F>, |
| { |
| <span class="doccomment">/// Construct a new `Dirichlet` with the given alpha parameter `alpha`. |
| /// |
| /// Requires `alpha.len() >= 2`. |
| </span><span class="attribute">#[inline] |
| </span><span class="kw">pub fn </span>new(alpha: <span class="kw-2">&</span>[F]) -> <span class="prelude-ty">Result</span><Dirichlet<F>, Error> { |
| <span class="kw">if </span>alpha.len() < <span class="number">2 </span>{ |
| <span class="kw">return </span><span class="prelude-val">Err</span>(Error::AlphaTooShort); |
| } |
| <span class="kw">for </span><span class="kw-2">&</span>ai <span class="kw">in </span>alpha.iter() { |
| <span class="kw">if </span>!(ai > F::zero()) { |
| <span class="kw">return </span><span class="prelude-val">Err</span>(Error::AlphaTooSmall); |
| } |
| } |
| |
| <span class="prelude-val">Ok</span>(Dirichlet { alpha: alpha.to_vec().into_boxed_slice() }) |
| } |
| |
| <span class="doccomment">/// Construct a new `Dirichlet` with the given shape parameter `alpha` and `size`. |
| /// |
| /// Requires `size >= 2`. |
| </span><span class="attribute">#[inline] |
| </span><span class="kw">pub fn </span>new_with_size(alpha: F, size: usize) -> <span class="prelude-ty">Result</span><Dirichlet<F>, Error> { |
| <span class="kw">if </span>!(alpha > F::zero()) { |
| <span class="kw">return </span><span class="prelude-val">Err</span>(Error::AlphaTooSmall); |
| } |
| <span class="kw">if </span>size < <span class="number">2 </span>{ |
| <span class="kw">return </span><span class="prelude-val">Err</span>(Error::SizeTooSmall); |
| } |
| <span class="prelude-val">Ok</span>(Dirichlet { |
| alpha: <span class="macro">vec!</span>[alpha; size].into_boxed_slice(), |
| }) |
| } |
| } |
| |
| <span class="kw">impl</span><F> Distribution<Vec<F>> <span class="kw">for </span>Dirichlet<F> |
| <span class="kw">where |
| </span>F: Float, |
| StandardNormal: Distribution<F>, |
| Exp1: Distribution<F>, |
| Open01: Distribution<F>, |
| { |
| <span class="kw">fn </span>sample<R: Rng + <span class="question-mark">?</span>Sized>(<span class="kw-2">&</span><span class="self">self</span>, rng: <span class="kw-2">&mut </span>R) -> Vec<F> { |
| <span class="kw">let </span>n = <span class="self">self</span>.alpha.len(); |
| <span class="kw">let </span><span class="kw-2">mut </span>samples = <span class="macro">vec!</span>[F::zero(); n]; |
| <span class="kw">let </span><span class="kw-2">mut </span>sum = F::zero(); |
| |
| <span class="kw">for </span>(s, <span class="kw-2">&</span>a) <span class="kw">in </span>samples.iter_mut().zip(<span class="self">self</span>.alpha.iter()) { |
| <span class="kw">let </span>g = Gamma::new(a, F::one()).unwrap(); |
| <span class="kw-2">*</span>s = g.sample(rng); |
| sum = sum + (<span class="kw-2">*</span>s); |
| } |
| <span class="kw">let </span>invacc = F::one() / sum; |
| <span class="kw">for </span>s <span class="kw">in </span>samples.iter_mut() { |
| <span class="kw-2">*</span>s = (<span class="kw-2">*</span>s)<span class="kw-2">*</span>invacc; |
| } |
| samples |
| } |
| } |
| |
| <span class="attribute">#[cfg(test)] |
| </span><span class="kw">mod </span>test { |
| <span class="kw">use super</span>::<span class="kw-2">*</span>; |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_dirichlet() { |
| <span class="kw">let </span>d = Dirichlet::new(<span class="kw-2">&</span>[<span class="number">1.0</span>, <span class="number">2.0</span>, <span class="number">3.0</span>]).unwrap(); |
| <span class="kw">let </span><span class="kw-2">mut </span>rng = <span class="kw">crate</span>::test::rng(<span class="number">221</span>); |
| <span class="kw">let </span>samples = d.sample(<span class="kw-2">&mut </span>rng); |
| <span class="kw">let _</span>: Vec<f64> = samples |
| .into_iter() |
| .map(|x| { |
| <span class="macro">assert!</span>(x > <span class="number">0.0</span>); |
| x |
| }) |
| .collect(); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_dirichlet_with_param() { |
| <span class="kw">let </span>alpha = <span class="number">0.5f64</span>; |
| <span class="kw">let </span>size = <span class="number">2</span>; |
| <span class="kw">let </span>d = Dirichlet::new_with_size(alpha, size).unwrap(); |
| <span class="kw">let </span><span class="kw-2">mut </span>rng = <span class="kw">crate</span>::test::rng(<span class="number">221</span>); |
| <span class="kw">let </span>samples = d.sample(<span class="kw-2">&mut </span>rng); |
| <span class="kw">let _</span>: Vec<f64> = samples |
| .into_iter() |
| .map(|x| { |
| <span class="macro">assert!</span>(x > <span class="number">0.0</span>); |
| x |
| }) |
| .collect(); |
| } |
| |
| <span class="attribute">#[test] |
| #[should_panic] |
| </span><span class="kw">fn </span>test_dirichlet_invalid_length() { |
| Dirichlet::new_with_size(<span class="number">0.5f64</span>, <span class="number">1</span>).unwrap(); |
| } |
| |
| <span class="attribute">#[test] |
| #[should_panic] |
| </span><span class="kw">fn </span>test_dirichlet_invalid_alpha() { |
| Dirichlet::new_with_size(<span class="number">0.0f64</span>, <span class="number">2</span>).unwrap(); |
| } |
| } |
| </code></pre></div> |
| </section></div></main><div id="rustdoc-vars" data-root-path="../../" data-current-crate="rand_distr" data-themes="ayu,dark,light" data-resource-suffix="" data-rustdoc-version="1.66.0-nightly (5c8bff74b 2022-10-21)" ></div></body></html> |
