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Diffstat (limited to 'rand/rand_distr/src/normal.rs')
| -rw-r--r-- | rand/rand_distr/src/normal.rs | 219 |
1 files changed, 0 insertions, 219 deletions
diff --git a/rand/rand_distr/src/normal.rs b/rand/rand_distr/src/normal.rs deleted file mode 100644 index 882754f..0000000 --- a/rand/rand_distr/src/normal.rs +++ /dev/null @@ -1,219 +0,0 @@ -// 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 normal and derived distributions. - -use rand::Rng; -use crate::{ziggurat_tables, Distribution, Open01}; -use crate::utils::{ziggurat, Float}; - -/// Samples floating-point numbers according to the normal distribution -/// `N(0, 1)` (a.k.a. a standard normal, or Gaussian). This is equivalent to -/// `Normal::new(0.0, 1.0)` but faster. -/// -/// See `Normal` for the general normal distribution. -/// -/// Implemented via the ZIGNOR variant[^1] of the Ziggurat method. -/// -/// [^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::StandardNormal; -/// -/// let val: f64 = thread_rng().sample(StandardNormal); -/// println!("{}", val); -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct StandardNormal; - -impl Distribution<f32> for StandardNormal { - #[inline] - fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f32 { - // TODO: use optimal 32-bit implementation - let x: f64 = self.sample(rng); - x as f32 - } -} - -impl Distribution<f64> for StandardNormal { - fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { - #[inline] - fn pdf(x: f64) -> f64 { - (-x*x/2.0).exp() - } - #[inline] - fn zero_case<R: Rng + ?Sized>(rng: &mut R, u: f64) -> f64 { - // compute a random number in the tail by hand - - // strange initial conditions, because the loop is not - // do-while, so the condition should be true on the first - // run, they get overwritten anyway (0 < 1, so these are - // good). - let mut x = 1.0f64; - let mut y = 0.0f64; - - while -2.0 * y < x * x { - let x_: f64 = rng.sample(Open01); - let y_: f64 = rng.sample(Open01); - - x = x_.ln() / ziggurat_tables::ZIG_NORM_R; - y = y_.ln(); - } - - if u < 0.0 { x - ziggurat_tables::ZIG_NORM_R } else { ziggurat_tables::ZIG_NORM_R - x } - } - - ziggurat(rng, true, // this is symmetric - &ziggurat_tables::ZIG_NORM_X, - &ziggurat_tables::ZIG_NORM_F, - pdf, zero_case) - } -} - -/// The normal distribution `N(mean, std_dev**2)`. -/// -/// This uses the ZIGNOR variant of the Ziggurat method, see [`StandardNormal`] -/// for more details. -/// -/// Note that [`StandardNormal`] is an optimised implementation for mean 0, and -/// standard deviation 1. -/// -/// # Example -/// -/// ``` -/// use rand_distr::{Normal, Distribution}; -/// -/// // mean 2, standard deviation 3 -/// let normal = Normal::new(2.0, 3.0).unwrap(); -/// let v = normal.sample(&mut rand::thread_rng()); -/// println!("{} is from a N(2, 9) distribution", v) -/// ``` -/// -/// [`StandardNormal`]: crate::StandardNormal -#[derive(Clone, Copy, Debug)] -pub struct Normal<N> { - mean: N, - std_dev: N, -} - -/// Error type returned from `Normal::new` and `LogNormal::new`. -#[derive(Clone, Copy, Debug, PartialEq, Eq)] -pub enum Error { - /// `std_dev < 0` or `nan`. - StdDevTooSmall, -} - -impl<N: Float> Normal<N> -where StandardNormal: Distribution<N> -{ - /// Construct a new `Normal` distribution with the given mean and - /// standard deviation. - #[inline] - pub fn new(mean: N, std_dev: N) -> Result<Normal<N>, Error> { - if !(std_dev >= N::from(0.0)) { - return Err(Error::StdDevTooSmall); - } - Ok(Normal { - mean, - std_dev - }) - } -} - -impl<N: Float> Distribution<N> for Normal<N> -where StandardNormal: Distribution<N> -{ - fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N { - let n: N = rng.sample(StandardNormal); - self.mean + self.std_dev * n - } -} - - -/// The log-normal distribution `ln N(mean, std_dev**2)`. -/// -/// If `X` is log-normal distributed, then `ln(X)` is `N(mean, std_dev**2)` -/// distributed. -/// -/// # Example -/// -/// ``` -/// use rand_distr::{LogNormal, Distribution}; -/// -/// // mean 2, standard deviation 3 -/// let log_normal = LogNormal::new(2.0, 3.0).unwrap(); -/// let v = log_normal.sample(&mut rand::thread_rng()); -/// println!("{} is from an ln N(2, 9) distribution", v) -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct LogNormal<N> { - norm: Normal<N> -} - -impl<N: Float> LogNormal<N> -where StandardNormal: Distribution<N> -{ - /// Construct a new `LogNormal` distribution with the given mean - /// and standard deviation of the logarithm of the distribution. - #[inline] - pub fn new(mean: N, std_dev: N) -> Result<LogNormal<N>, Error> { - if !(std_dev >= N::from(0.0)) { - return Err(Error::StdDevTooSmall); - } - Ok(LogNormal { norm: Normal::new(mean, std_dev).unwrap() }) - } -} - -impl<N: Float> Distribution<N> for LogNormal<N> -where StandardNormal: Distribution<N> -{ - fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N { - self.norm.sample(rng).exp() - } -} - -#[cfg(test)] -mod tests { - use crate::Distribution; - use super::{Normal, LogNormal}; - - #[test] - fn test_normal() { - let norm = Normal::new(10.0, 10.0).unwrap(); - let mut rng = crate::test::rng(210); - for _ in 0..1000 { - norm.sample(&mut rng); - } - } - #[test] - #[should_panic] - fn test_normal_invalid_sd() { - Normal::new(10.0, -1.0).unwrap(); - } - - - #[test] - fn test_log_normal() { - let lnorm = LogNormal::new(10.0, 10.0).unwrap(); - let mut rng = crate::test::rng(211); - for _ in 0..1000 { - lnorm.sample(&mut rng); - } - } - #[test] - #[should_panic] - fn test_log_normal_invalid_sd() { - LogNormal::new(10.0, -1.0).unwrap(); - } -} |