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authorValentin Popov <valentin@popov.link>2024-01-08 00:21:28 +0300
committerValentin Popov <valentin@popov.link>2024-01-08 00:21:28 +0300
commit1b6a04ca5504955c571d1c97504fb45ea0befee4 (patch)
tree7579f518b23313e8a9748a88ab6173d5e030b227 /vendor/rand/src/distributions/exponential.rs
parent5ecd8cf2cba827454317368b68571df0d13d7842 (diff)
downloadfparkan-1b6a04ca5504955c571d1c97504fb45ea0befee4.tar.xz
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Initial vendor packages
Signed-off-by: Valentin Popov <valentin@popov.link>
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+// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
+// file at the top-level directory of this distribution and at
+// http://rust-lang.org/COPYRIGHT.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The exponential distribution.
+
+use {Rng, Rand};
+use distributions::{ziggurat, ziggurat_tables, Sample, IndependentSample};
+
+/// A wrapper around an `f64` to generate Exp(1) random numbers.
+///
+/// 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*](http://www.doornik.com/research/ziggurat.pdf). Nuffield
+/// College, Oxford
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::exponential::Exp1;
+///
+/// let Exp1(x) = rand::random();
+/// println!("{}", x);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Exp1(pub f64);
+
+// This could be done via `-rng.gen::<f64>().ln()` but that is slower.
+impl Rand for Exp1 {
+ #[inline]
+ fn rand<R:Rng>(rng: &mut R) -> Exp1 {
+ #[inline]
+ fn pdf(x: f64) -> f64 {
+ (-x).exp()
+ }
+ #[inline]
+ fn zero_case<R:Rng>(rng: &mut R, _u: f64) -> f64 {
+ ziggurat_tables::ZIG_EXP_R - rng.gen::<f64>().ln()
+ }
+
+ Exp1(ziggurat(rng, false,
+ &ziggurat_tables::ZIG_EXP_X,
+ &ziggurat_tables::ZIG_EXP_F,
+ pdf, zero_case))
+ }
+}
+
+/// The exponential distribution `Exp(lambda)`.
+///
+/// This distribution has density function: `f(x) = lambda *
+/// exp(-lambda * x)` for `x > 0`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{Exp, IndependentSample};
+///
+/// let exp = Exp::new(2.0);
+/// let v = exp.ind_sample(&mut rand::thread_rng());
+/// println!("{} is from a Exp(2) distribution", v);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Exp {
+ /// `lambda` stored as `1/lambda`, since this is what we scale by.
+ lambda_inverse: f64
+}
+
+impl Exp {
+ /// Construct a new `Exp` with the given shape parameter
+ /// `lambda`. Panics if `lambda <= 0`.
+ #[inline]
+ pub fn new(lambda: f64) -> Exp {
+ assert!(lambda > 0.0, "Exp::new called with `lambda` <= 0");
+ Exp { lambda_inverse: 1.0 / lambda }
+ }
+}
+
+impl Sample<f64> for Exp {
+ fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) }
+}
+impl IndependentSample<f64> for Exp {
+ fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
+ let Exp1(n) = rng.gen::<Exp1>();
+ n * self.lambda_inverse
+ }
+}
+
+#[cfg(test)]
+mod test {
+ use distributions::{Sample, IndependentSample};
+ use super::Exp;
+
+ #[test]
+ fn test_exp() {
+ let mut exp = Exp::new(10.0);
+ let mut rng = ::test::rng();
+ for _ in 0..1000 {
+ assert!(exp.sample(&mut rng) >= 0.0);
+ assert!(exp.ind_sample(&mut rng) >= 0.0);
+ }
+ }
+ #[test]
+ #[should_panic]
+ fn test_exp_invalid_lambda_zero() {
+ Exp::new(0.0);
+ }
+ #[test]
+ #[should_panic]
+ fn test_exp_invalid_lambda_neg() {
+ Exp::new(-10.0);
+ }
+}