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std::rand: move normal and exponential to their own file.

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This commit is contained in:
Huon Wilson 2013-12-07 22:14:20 +11:00
parent 3abc350ea4
commit 9d5639d11b
4 changed files with 303 additions and 244 deletions
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141
src/libstd/rand/distributions/exponential.rs Normal file
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@ -0,0 +1,141 @@
// 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 rand::{Rng, Rand};
use rand::distributions::{ziggurat, ziggurat_tables, Sample, IndependentSample};
/// A wrapper around an `f64` to generate Exp(1) random numbers.
///
/// See `Exp` for the general exponential distribution.Note that this
// has to be unwrapped before use as an `f64` (using either
/// `*` or `cast::transmute` is safe).
///
/// 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
pub struct Exp1(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 std::rand;
/// use std::rand::distributions::{Exp, IndependentSample};
///
/// fn main() {
/// let exp = Exp::new(2.0);
/// let v = exp.ind_sample(&mut rand::task_rng());
/// println!("{} is from a Exp(2) distribution", v);
/// }
/// ```
pub struct Exp {
/// `lambda` stored as `1/lambda`, since this is what we scale by.
priv lambda_inverse: f64
}
impl Exp {
/// Construct a new `Exp` with the given shape parameter
/// `lambda`. Fails if `lambda <= 0`.
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 {
(*rng.gen::<Exp1>()) * self.lambda_inverse
}
}
#[cfg(test)]
mod test {
use rand::*;
use super::*;
use iter::range;
use option::{Some, None};
#[test]
fn test_exp() {
let mut exp = Exp::new(10.0);
let mut rng = task_rng();
for _ in range(0, 1000) {
assert!(exp.sample(&mut rng) >= 0.0);
assert!(exp.ind_sample(&mut rng) >= 0.0);
}
}
#[test]
#[should_fail]
fn test_exp_invalid_lambda_zero() {
Exp::new(0.0);
}
#[test]
#[should_fail]
fn test_exp_invalid_lambda_neg() {
Exp::new(-10.0);
}
}
#[cfg(test)]
mod bench {
use extra::test::BenchHarness;
use rand::{XorShiftRng, RAND_BENCH_N};
use super::*;
use iter::range;
use option::{Some, None};
use mem::size_of;
#[bench]
fn rand_exp(bh: &mut BenchHarness) {
let mut rng = XorShiftRng::new();
let mut exp = Exp::new(2.71828 * 3.14159);
bh.iter(|| {
for _ in range(0, RAND_BENCH_N) {
exp.sample(&mut rng);
}
});
bh.bytes = size_of::<f64>() as u64 * RAND_BENCH_N;
}
}

3
src/libstd/rand/distributions/gamma.rs
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@ -11,7 +11,8 @@
//! The Gamma distribution.
use rand::{Rng, Open01};
use super::{IndependentSample, Sample, StandardNormal, Exp};
use super::{IndependentSample, Sample, Exp};
use super::normal::StandardNormal;
use num;
/// The Gamma distribution `Gamma(shape, scale)` distribution.

248
src/libstd/rand/distributions/mod.rs
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@ -23,14 +23,18 @@
use iter::range;
use option::{Some, None};
use num;
use rand::{Rng, Rand, Open01};
use rand::{Rng, Rand};
use clone::Clone;
pub use self::range::Range;
pub use self::gamma::Gamma;
pub use self::normal::Normal;
pub use self::exponential::Exp;
pub mod range;
pub mod gamma;
pub mod normal;
pub mod exponential;
/// Types that can be used to create a random instance of `Support`.
pub trait Sample<Support> {
@ -246,181 +250,10 @@ fn ziggurat<R:Rng>(
}
}
/// A wrapper around an `f64` to generate N(0, 1) random numbers
/// (a.k.a. a standard normal, or Gaussian).
///
/// See `Normal` for the general normal distribution. That this has to
/// be unwrapped before use as an `f64` (using either `*` or
/// `cast::transmute` is safe).
///
/// 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
pub struct StandardNormal(f64);
impl Rand for StandardNormal {
fn rand<R:Rng>(rng: &mut R) -> StandardNormal {
#[inline]
fn pdf(x: f64) -> f64 {
((-x*x/2.0) as f64).exp()
}
#[inline]
fn zero_case<R:Rng>(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_ = *rng.gen::<Open01<f64>>();
let y_ = *rng.gen::<Open01<f64>>();
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 }
}
StandardNormal(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.
///
/// # Example
///
/// ```
/// use std::rand;
/// use std::rand::distributions::{Normal, IndependentSample};
///
/// fn main() {
/// let normal = Normal::new(2.0, 3.0);
/// let v = normal.ind_sample(rand::task_rng());
/// println!("{} is from a N(2, 9) distribution", v)
/// }
/// ```
pub struct Normal {
priv mean: f64,
priv std_dev: f64
}
impl Normal {
/// Construct a new `Normal` distribution with the given mean and
/// standard deviation. Fails if `std_dev < 0`.
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
}
}
}
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 {
self.mean + self.std_dev * (*rng.gen::<StandardNormal>())
}
}
/// A wrapper around an `f64` to generate Exp(1) random numbers.
///
/// See `Exp` for the general exponential distribution.Note that this
// has to be unwrapped before use as an `f64` (using either
/// `*` or `cast::transmute` is safe).
///
/// 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
pub struct Exp1(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
///
/// ```
/// use std::rand;
/// use std::rand::distributions::{Exp, IndependentSample};
///
/// fn main() {
/// let exp = Exp::new(2.0);
/// let v = exp.ind_sample(rand::task_rng());
/// println!("{} is from a Exp(2) distribution", v);
/// }
/// ```
pub struct Exp {
/// `lambda` stored as `1/lambda`, since this is what we scale by.
priv lambda_inverse: f64
}
impl Exp {
/// Construct a new `Exp` with the given shape parameter
/// `lambda`. Fails if `lambda <= 0`.
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 {
(*rng.gen::<Exp1>()) * self.lambda_inverse
}
}
#[cfg(test)]
mod tests {
use rand::*;
use super::*;
use iter::range;
use option::{Some, None};
struct ConstRand(uint);
@ -449,42 +282,6 @@ fn test_rand_sample() {
assert_eq!(*rand_sample.sample(&mut task_rng()), 0);
assert_eq!(*rand_sample.ind_sample(&mut task_rng()), 0);
}
#[test]
fn test_normal() {
let mut norm = Normal::new(10.0, 10.0);
let mut rng = task_rng();
for _ in range(0, 1000) {
norm.sample(&mut rng);
norm.ind_sample(&mut rng);
}
}
#[test]
#[should_fail]
fn test_normal_invalid_sd() {
Normal::new(10.0, -1.0);
}
#[test]
fn test_exp() {
let mut exp = Exp::new(10.0);
let mut rng = task_rng();
for _ in range(0, 1000) {
assert!(exp.sample(&mut rng) >= 0.0);
assert!(exp.ind_sample(&mut rng) >= 0.0);
}
}
#[test]
#[should_fail]
fn test_exp_invalid_lambda_zero() {
Exp::new(0.0);
}
#[test]
#[should_fail]
fn test_exp_invalid_lambda_neg() {
Exp::new(-10.0);
}
#[test]
fn test_weighted_choice() {
// this makes assumptions about the internal implementation of
@ -556,38 +353,3 @@ fn test_weighted_choice_weight_overflows() {
Weighted { weight: 1, item: 3 }]);
}
}
#[cfg(test)]
mod bench {
use extra::test::BenchHarness;
use rand::{XorShiftRng, RAND_BENCH_N};
use super::*;
use iter::range;
use option::{Some, None};
use mem::size_of;
#[bench]
fn rand_normal(bh: &mut BenchHarness) {
let mut rng = XorShiftRng::new();
let mut normal = Normal::new(-2.71828, 3.14159);
bh.iter(|| {
for _ in range(0, RAND_BENCH_N) {
normal.sample(&mut rng);
}
});
bh.bytes = size_of::<f64>() as u64 * RAND_BENCH_N;
}
#[bench]
fn rand_exp(bh: &mut BenchHarness) {
let mut rng = XorShiftRng::new();
let mut exp = Exp::new(2.71828 * 3.14159);
bh.iter(|| {
for _ in range(0, RAND_BENCH_N) {
exp.sample(&mut rng);
}
});
bh.bytes = size_of::<f64>() as u64 * RAND_BENCH_N;
}
}

155
src/libstd/rand/distributions/normal.rs Normal file
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@ -0,0 +1,155 @@
// 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 normal distribution.
use rand::{Rng, Rand, Open01};
use rand::distributions::{ziggurat, ziggurat_tables, Sample, IndependentSample};
/// A wrapper around an `f64` to generate N(0, 1) random numbers
/// (a.k.a. a standard normal, or Gaussian).
///
/// See `Normal` for the general normal distribution. That this has to
/// be unwrapped before use as an `f64` (using either `*` or
/// `cast::transmute` is safe).
///
/// 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
pub struct StandardNormal(f64);
impl Rand for StandardNormal {
fn rand<R:Rng>(rng: &mut R) -> StandardNormal {
#[inline]
fn pdf(x: f64) -> f64 {
((-x*x/2.0) as f64).exp()
}
#[inline]
fn zero_case<R:Rng>(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_ = *rng.gen::<Open01<f64>>();
let y_ = *rng.gen::<Open01<f64>>();
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 }
}
StandardNormal(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.
///
/// # Example
///
/// ```rust
/// use std::rand;
/// use std::rand::distributions::{Normal, IndependentSample};
///
/// fn main() {
/// // mean 2, standard deviation 3
/// let normal = Normal::new(2.0, 3.0);
/// let v = normal.ind_sample(&mut rand::task_rng());
/// println!("{} is from a N(2, 9) distribution", v)
/// }
/// ```
pub struct Normal {
priv mean: f64,
priv std_dev: f64
}
impl Normal {
/// Construct a new `Normal` distribution with the given mean and
/// standard deviation. Fails if `std_dev < 0`.
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
}
}
}
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 {
self.mean + self.std_dev * (*rng.gen::<StandardNormal>())
}
}
#[cfg(test)]
mod tests {
use rand::*;
use super::*;
use iter::range;
use option::{Some, None};
#[test]
fn test_normal() {
let mut norm = Normal::new(10.0, 10.0);
let mut rng = task_rng();
for _ in range(0, 1000) {
norm.sample(&mut rng);
norm.ind_sample(&mut rng);
}
}
#[test]
#[should_fail]
fn test_normal_invalid_sd() {
Normal::new(10.0, -1.0);
}
}
#[cfg(test)]
mod bench {
use extra::test::BenchHarness;
use rand::{XorShiftRng, RAND_BENCH_N};
use super::*;
use iter::range;
use option::{Some, None};
use mem::size_of;
#[bench]
fn rand_normal(bh: &mut BenchHarness) {
let mut rng = XorShiftRng::new();
let mut normal = Normal::new(-2.71828, 3.14159);
bh.iter(|| {
for _ in range(0, RAND_BENCH_N) {
normal.sample(&mut rng);
}
});
bh.bytes = size_of::<f64>() as u64 * RAND_BENCH_N;
}
}