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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 exponential distribution. |
| |
| </span><span class="kw">use </span><span class="kw">crate</span>::utils::ziggurat; |
| <span class="kw">use </span>num_traits::Float; |
| <span class="kw">use crate</span>::{ziggurat_tables, Distribution}; |
| <span class="kw">use </span>rand::Rng; |
| <span class="kw">use </span>core::fmt; |
| |
| <span class="doccomment">/// Samples floating-point numbers according to the exponential distribution, |
| /// with rate parameter `λ = 1`. This is equivalent to `Exp::new(1.0)` or |
| /// sampling with `-rng.gen::<f64>().ln()`, but faster. |
| /// |
| /// See `Exp` for the general exponential distribution. |
| /// |
| /// Implemented via the ZIGNOR variant[^1] of the Ziggurat method. The exact |
| /// description in the paper was adjusted to use tables for the exponential |
| /// distribution rather than normal. |
| /// |
| /// [^1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to |
| /// Generate Normal Random Samples*]( |
| /// https://www.doornik.com/research/ziggurat.pdf). |
| /// Nuffield College, Oxford |
| /// |
| /// # Example |
| /// ``` |
| /// use rand::prelude::*; |
| /// use rand_distr::Exp1; |
| /// |
| /// let val: f64 = thread_rng().sample(Exp1); |
| /// println!("{}", val); |
| /// ``` |
| </span><span class="attribute">#[derive(Clone, Copy, Debug)] |
| #[cfg_attr(feature = <span class="string">"serde1"</span>, derive(serde::Serialize, serde::Deserialize))] |
| </span><span class="kw">pub struct </span>Exp1; |
| |
| <span class="kw">impl </span>Distribution<f32> <span class="kw">for </span>Exp1 { |
| <span class="attribute">#[inline] |
| </span><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) -> f32 { |
| <span class="comment">// TODO: use optimal 32-bit implementation |
| </span><span class="kw">let </span>x: f64 = <span class="self">self</span>.sample(rng); |
| x <span class="kw">as </span>f32 |
| } |
| } |
| |
| <span class="comment">// This could be done via `-rng.gen::<f64>().ln()` but that is slower. |
| </span><span class="kw">impl </span>Distribution<f64> <span class="kw">for </span>Exp1 { |
| <span class="attribute">#[inline] |
| </span><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) -> f64 { |
| <span class="attribute">#[inline] |
| </span><span class="kw">fn </span>pdf(x: f64) -> f64 { |
| (-x).exp() |
| } |
| <span class="attribute">#[inline] |
| </span><span class="kw">fn </span>zero_case<R: Rng + <span class="question-mark">?</span>Sized>(rng: <span class="kw-2">&mut </span>R, _u: f64) -> f64 { |
| ziggurat_tables::ZIG_EXP_R - rng.gen::<f64>().ln() |
| } |
| |
| ziggurat( |
| rng, |
| <span class="bool-val">false</span>, |
| <span class="kw-2">&</span>ziggurat_tables::ZIG_EXP_X, |
| <span class="kw-2">&</span>ziggurat_tables::ZIG_EXP_F, |
| pdf, |
| zero_case, |
| ) |
| } |
| } |
| |
| <span class="doccomment">/// The exponential distribution `Exp(lambda)`. |
| /// |
| /// This distribution has density function: `f(x) = lambda * exp(-lambda * x)` |
| /// for `x > 0`, when `lambda > 0`. For `lambda = 0`, all samples yield infinity. |
| /// |
| /// Note that [`Exp1`](crate::Exp1) is an optimised implementation for `lambda = 1`. |
| /// |
| /// # Example |
| /// |
| /// ``` |
| /// use rand_distr::{Exp, Distribution}; |
| /// |
| /// let exp = Exp::new(2.0).unwrap(); |
| /// let v = exp.sample(&mut rand::thread_rng()); |
| /// println!("{} is from a Exp(2) distribution", v); |
| /// ``` |
| </span><span class="attribute">#[derive(Clone, Copy, Debug)] |
| #[cfg_attr(feature = <span class="string">"serde1"</span>, derive(serde::Serialize, serde::Deserialize))] |
| </span><span class="kw">pub struct </span>Exp<F> |
| <span class="kw">where </span>F: Float, Exp1: Distribution<F> |
| { |
| <span class="doccomment">/// `lambda` stored as `1/lambda`, since this is what we scale by. |
| </span>lambda_inverse: F, |
| } |
| |
| <span class="doccomment">/// Error type returned from `Exp::new`. |
| </span><span class="attribute">#[derive(Clone, Copy, Debug, PartialEq, Eq)] |
| </span><span class="kw">pub enum </span>Error { |
| <span class="doccomment">/// `lambda < 0` or `nan`. |
| </span>LambdaTooSmall, |
| } |
| |
| <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::LambdaTooSmall => <span class="string">"lambda is negative or NaN in exponential 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: Float> Exp<F> |
| <span class="kw">where </span>F: Float, Exp1: Distribution<F> |
| { |
| <span class="doccomment">/// Construct a new `Exp` with the given shape parameter |
| /// `lambda`. |
| /// |
| /// # Remarks |
| /// |
| /// For custom types `N` implementing the [`Float`] trait, |
| /// the case `lambda = 0` is handled as follows: each sample corresponds |
| /// to a sample from an `Exp1` multiplied by `1 / 0`. Primitive types |
| /// yield infinity, since `1 / 0 = infinity`. |
| </span><span class="attribute">#[inline] |
| </span><span class="kw">pub fn </span>new(lambda: F) -> <span class="prelude-ty">Result</span><Exp<F>, Error> { |
| <span class="kw">if </span>!(lambda >= F::zero()) { |
| <span class="kw">return </span><span class="prelude-val">Err</span>(Error::LambdaTooSmall); |
| } |
| <span class="prelude-val">Ok</span>(Exp { |
| lambda_inverse: F::one() / lambda, |
| }) |
| } |
| } |
| |
| <span class="kw">impl</span><F> Distribution<F> <span class="kw">for </span>Exp<F> |
| <span class="kw">where </span>F: Float, Exp1: 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) -> F { |
| rng.sample(Exp1) * <span class="self">self</span>.lambda_inverse |
| } |
| } |
| |
| <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_exp() { |
| <span class="kw">let </span>exp = Exp::new(<span class="number">10.0</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">for _ in </span><span class="number">0</span>..<span class="number">1000 </span>{ |
| <span class="macro">assert!</span>(exp.sample(<span class="kw-2">&mut </span>rng) >= <span class="number">0.0</span>); |
| } |
| } |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_zero() { |
| <span class="kw">let </span>d = Exp::new(<span class="number">0.0</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(d.sample(<span class="kw-2">&mut </span><span class="kw">crate</span>::test::rng(<span class="number">21</span>)), f64::infinity()); |
| } |
| <span class="attribute">#[test] |
| #[should_panic] |
| </span><span class="kw">fn </span>test_exp_invalid_lambda_neg() { |
| Exp::new(-<span class="number">10.0</span>).unwrap(); |
| } |
| |
| <span class="attribute">#[test] |
| #[should_panic] |
| </span><span class="kw">fn </span>test_exp_invalid_lambda_nan() { |
| Exp::new(f64::nan()).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> |
