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-rw-r--r--rand/src/distributions/normal.rs120
1 files changed, 58 insertions, 62 deletions
diff --git a/rand/src/distributions/normal.rs b/rand/src/distributions/normal.rs
index 280613d..b8d632e 100644
--- a/rand/src/distributions/normal.rs
+++ b/rand/src/distributions/normal.rs
@@ -1,49 +1,50 @@
-// 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.
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
//
// 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
+// 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 {Rng, Rand, Open01};
-use distributions::{ziggurat, ziggurat_tables, Sample, IndependentSample};
+use Rng;
+use distributions::{ziggurat_tables, Distribution, Open01};
+use distributions::utils::ziggurat;
-/// A wrapper around an `f64` to generate N(0, 1) random numbers
-/// (a.k.a. a standard normal, or Gaussian).
+/// 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.
+/// Implemented via the ZIGNOR variant[^1] of the Ziggurat method.
///
-/// [1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to
-/// Generate Normal Random
-/// Samples*](http://www.doornik.com/research/ziggurat.pdf). Nuffield
-/// College, Oxford
+/// [^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::distributions::StandardNormal;
///
-/// ```rust
-/// use rand::distributions::normal::StandardNormal;
-///
-/// let StandardNormal(x) = rand::random();
-/// println!("{}", x);
+/// let val: f64 = SmallRng::from_entropy().sample(StandardNormal);
+/// println!("{}", val);
/// ```
#[derive(Clone, Copy, Debug)]
-pub struct StandardNormal(pub f64);
+pub struct StandardNormal;
-impl Rand for StandardNormal {
- fn rand<R:Rng>(rng: &mut R) -> StandardNormal {
+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>(rng: &mut R, u: f64) -> f64 {
+ 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
@@ -54,8 +55,8 @@ impl Rand for StandardNormal {
let mut y = 0.0f64;
while -2.0 * y < x * x {
- let Open01(x_) = rng.gen::<Open01<f64>>();
- let Open01(y_) = rng.gen::<Open01<f64>>();
+ let x_: f64 = rng.sample(Open01);
+ let y_: f64 = rng.sample(Open01);
x = x_.ln() / ziggurat_tables::ZIG_NORM_R;
y = y_.ln();
@@ -64,30 +65,33 @@ impl Rand for StandardNormal {
if u < 0.0 { x - ziggurat_tables::ZIG_NORM_R } else { ziggurat_tables::ZIG_NORM_R - x }
}
- StandardNormal(ziggurat(
- rng,
- true, // this is symmetric
- &ziggurat_tables::ZIG_NORM_X,
- &ziggurat_tables::ZIG_NORM_F,
- pdf, zero_case))
+ 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.
+/// 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
///
-/// ```rust
-/// use rand::distributions::{Normal, IndependentSample};
+/// ```
+/// use rand::distributions::{Normal, Distribution};
///
/// // mean 2, standard deviation 3
/// let normal = Normal::new(2.0, 3.0);
-/// let v = normal.ind_sample(&mut rand::thread_rng());
+/// let v = normal.sample(&mut rand::thread_rng());
/// println!("{} is from a N(2, 9) distribution", v)
/// ```
+///
+/// [`StandardNormal`]: struct.StandardNormal.html
#[derive(Clone, Copy, Debug)]
pub struct Normal {
mean: f64,
@@ -105,17 +109,14 @@ impl Normal {
pub fn new(mean: f64, std_dev: f64) -> Normal {
assert!(std_dev >= 0.0, "Normal::new called with `std_dev` < 0");
Normal {
- mean: mean,
- std_dev: std_dev
+ mean,
+ std_dev
}
}
}
-impl Sample<f64> for Normal {
- fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) }
-}
-impl IndependentSample<f64> for Normal {
- fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
- let StandardNormal(n) = rng.gen::<StandardNormal>();
+impl Distribution<f64> for Normal {
+ fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+ let n = rng.sample(StandardNormal);
self.mean + self.std_dev * n
}
}
@@ -123,17 +124,17 @@ impl IndependentSample<f64> for Normal {
/// 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.
+/// If `X` is log-normal distributed, then `ln(X)` is `N(mean, std_dev**2)`
+/// distributed.
///
/// # Example
///
-/// ```rust
-/// use rand::distributions::{LogNormal, IndependentSample};
+/// ```
+/// use rand::distributions::{LogNormal, Distribution};
///
/// // mean 2, standard deviation 3
/// let log_normal = LogNormal::new(2.0, 3.0);
-/// let v = log_normal.ind_sample(&mut rand::thread_rng());
+/// let v = log_normal.sample(&mut rand::thread_rng());
/// println!("{} is from an ln N(2, 9) distribution", v)
/// ```
#[derive(Clone, Copy, Debug)]
@@ -154,27 +155,23 @@ impl LogNormal {
LogNormal { norm: Normal::new(mean, std_dev) }
}
}
-impl Sample<f64> for LogNormal {
- fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) }
-}
-impl IndependentSample<f64> for LogNormal {
- fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
- self.norm.ind_sample(rng).exp()
+impl Distribution<f64> for LogNormal {
+ fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+ self.norm.sample(rng).exp()
}
}
#[cfg(test)]
mod tests {
- use distributions::{Sample, IndependentSample};
+ use distributions::Distribution;
use super::{Normal, LogNormal};
#[test]
fn test_normal() {
- let mut norm = Normal::new(10.0, 10.0);
- let mut rng = ::test::rng();
+ let norm = Normal::new(10.0, 10.0);
+ let mut rng = ::test::rng(210);
for _ in 0..1000 {
norm.sample(&mut rng);
- norm.ind_sample(&mut rng);
}
}
#[test]
@@ -186,11 +183,10 @@ mod tests {
#[test]
fn test_log_normal() {
- let mut lnorm = LogNormal::new(10.0, 10.0);
- let mut rng = ::test::rng();
+ let lnorm = LogNormal::new(10.0, 10.0);
+ let mut rng = ::test::rng(211);
for _ in 0..1000 {
lnorm.sample(&mut rng);
- lnorm.ind_sample(&mut rng);
}
}
#[test]
generated by cgit v1.2.1 (git 2.18.0) at 2024-05-19 06:58:36 +0000