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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 or the MIT license // , 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::().ln()` but that is slower. impl Rand for Exp1 { #[inline] fn rand(rng: &mut R) -> Exp1 { #[inline] fn pdf(x: f64) -> f64 { (-x).exp() } #[inline] fn zero_case(rng: &mut R, _u: f64) -> f64 { ziggurat_tables::ZIG_EXP_R - rng.gen::().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 for Exp { fn sample(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } } impl IndependentSample for Exp { fn ind_sample(&self, rng: &mut R) -> f64 { let Exp1(n) = rng.gen::(); 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); } }