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authorRobin Krahl <me@robin-krahl.de>2018-12-11 23:50:45 +0100
committerDaniel Mueller <deso@posteo.net>2018-12-17 07:52:13 -0800
commit986ad2f782cf944990e4eda8bf88ea1821233302 (patch)
tree1717075a4eb11861c32e5c45d01e47360fb1264d /rand/src
parente97c287c01cf22a1b582a7da9b309b58f3935d0e (diff)
downloadnitrocli-986ad2f782cf944990e4eda8bf88ea1821233302.tar.gz
nitrocli-986ad2f782cf944990e4eda8bf88ea1821233302.tar.bz2
Add nitrokey as a dependency to nitrocli
The nitrokey crate provides a simple interface to the Nitrokey Storage and the Nitrokey Pro based on the libnitrokey library developed by Nitrokey UG. The low-level bindings to this library are available in the nitrokey-sys crate. This patch adds version v0.2.1 of the nitrokey crate as a dependency for nitrocli. It includes the indirect dependencies nitrokey-sys (version 3.4.1) and rand (version 0.4.3). Import subrepo nitrokey/:nitrokey at 2eccc96ceec2282b868891befe9cda7f941fbe7b Import subrepo nitrokey-sys/:nitrokey-sys at f1a11ebf72610fb9cf80ac7f9f147b4ba1a5336f Import subrepo rand/:rand at d7d5da49daf7ceb3e5940072940d495cced3a1b3
Diffstat (limited to 'rand/src')
-rw-r--r--rand/src/distributions/exponential.rs124
-rw-r--r--rand/src/distributions/gamma.rs386
-rw-r--r--rand/src/distributions/mod.rs409
-rw-r--r--rand/src/distributions/normal.rs201
-rw-r--r--rand/src/distributions/range.rs241
-rw-r--r--rand/src/distributions/ziggurat_tables.rs280
-rw-r--r--rand/src/jitter.rs754
-rw-r--r--rand/src/lib.rs1214
-rw-r--r--rand/src/os.rs617
-rw-r--r--rand/src/prng/chacha.rs321
-rw-r--r--rand/src/prng/isaac.rs328
-rw-r--r--rand/src/prng/isaac64.rs340
-rw-r--r--rand/src/prng/mod.rs51
-rw-r--r--rand/src/prng/xorshift.rs101
-rw-r--r--rand/src/rand_impls.rs299
-rw-r--r--rand/src/read.rs123
-rw-r--r--rand/src/reseeding.rs229
-rw-r--r--rand/src/seq.rs337
18 files changed, 6355 insertions, 0 deletions
diff --git a/rand/src/distributions/exponential.rs b/rand/src/distributions/exponential.rs
new file mode 100644
index 0000000..c3c924c
--- /dev/null
+++ b/rand/src/distributions/exponential.rs
@@ -0,0 +1,124 @@
+// 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);
+ }
+}
diff --git a/rand/src/distributions/gamma.rs b/rand/src/distributions/gamma.rs
new file mode 100644
index 0000000..2806495
--- /dev/null
+++ b/rand/src/distributions/gamma.rs
@@ -0,0 +1,386 @@
+// 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.
+//
+// ignore-lexer-test FIXME #15679
+
+//! The Gamma and derived distributions.
+
+use self::GammaRepr::*;
+use self::ChiSquaredRepr::*;
+
+use {Rng, Open01};
+use super::normal::StandardNormal;
+use super::{IndependentSample, Sample, Exp};
+
+/// The Gamma distribution `Gamma(shape, scale)` distribution.
+///
+/// The density function of this distribution is
+///
+/// ```text
+/// f(x) = x^(k - 1) * exp(-x / θ) / (Γ(k) * θ^k)
+/// ```
+///
+/// where `Γ` is the Gamma function, `k` is the shape and `θ` is the
+/// scale and both `k` and `θ` are strictly positive.
+///
+/// The algorithm used is that described by Marsaglia & Tsang 2000[1],
+/// falling back to directly sampling from an Exponential for `shape
+/// == 1`, and using the boosting technique described in [1] for
+/// `shape < 1`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{IndependentSample, Gamma};
+///
+/// let gamma = Gamma::new(2.0, 5.0);
+/// let v = gamma.ind_sample(&mut rand::thread_rng());
+/// println!("{} is from a Gamma(2, 5) distribution", v);
+/// ```
+///
+/// [1]: George Marsaglia and Wai Wan Tsang. 2000. "A Simple Method
+/// for Generating Gamma Variables" *ACM Trans. Math. Softw.* 26, 3
+/// (September 2000),
+/// 363-372. DOI:[10.1145/358407.358414](http://doi.acm.org/10.1145/358407.358414)
+#[derive(Clone, Copy, Debug)]
+pub struct Gamma {
+ repr: GammaRepr,
+}
+
+#[derive(Clone, Copy, Debug)]
+enum GammaRepr {
+ Large(GammaLargeShape),
+ One(Exp),
+ Small(GammaSmallShape)
+}
+
+// These two helpers could be made public, but saving the
+// match-on-Gamma-enum branch from using them directly (e.g. if one
+// knows that the shape is always > 1) doesn't appear to be much
+// faster.
+
+/// Gamma distribution where the shape parameter is less than 1.
+///
+/// Note, samples from this require a compulsory floating-point `pow`
+/// call, which makes it significantly slower than sampling from a
+/// gamma distribution where the shape parameter is greater than or
+/// equal to 1.
+///
+/// See `Gamma` for sampling from a Gamma distribution with general
+/// shape parameters.
+#[derive(Clone, Copy, Debug)]
+struct GammaSmallShape {
+ inv_shape: f64,
+ large_shape: GammaLargeShape
+}
+
+/// Gamma distribution where the shape parameter is larger than 1.
+///
+/// See `Gamma` for sampling from a Gamma distribution with general
+/// shape parameters.
+#[derive(Clone, Copy, Debug)]
+struct GammaLargeShape {
+ scale: f64,
+ c: f64,
+ d: f64
+}
+
+impl Gamma {
+ /// Construct an object representing the `Gamma(shape, scale)`
+ /// distribution.
+ ///
+ /// Panics if `shape <= 0` or `scale <= 0`.
+ #[inline]
+ pub fn new(shape: f64, scale: f64) -> Gamma {
+ assert!(shape > 0.0, "Gamma::new called with shape <= 0");
+ assert!(scale > 0.0, "Gamma::new called with scale <= 0");
+
+ let repr = if shape == 1.0 {
+ One(Exp::new(1.0 / scale))
+ } else if shape < 1.0 {
+ Small(GammaSmallShape::new_raw(shape, scale))
+ } else {
+ Large(GammaLargeShape::new_raw(shape, scale))
+ };
+ Gamma { repr: repr }
+ }
+}
+
+impl GammaSmallShape {
+ fn new_raw(shape: f64, scale: f64) -> GammaSmallShape {
+ GammaSmallShape {
+ inv_shape: 1. / shape,
+ large_shape: GammaLargeShape::new_raw(shape + 1.0, scale)
+ }
+ }
+}
+
+impl GammaLargeShape {
+ fn new_raw(shape: f64, scale: f64) -> GammaLargeShape {
+ let d = shape - 1. / 3.;
+ GammaLargeShape {
+ scale: scale,
+ c: 1. / (9. * d).sqrt(),
+ d: d
+ }
+ }
+}
+
+impl Sample<f64> for Gamma {
+ fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) }
+}
+impl Sample<f64> for GammaSmallShape {
+ fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) }
+}
+impl Sample<f64> for GammaLargeShape {
+ fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) }
+}
+
+impl IndependentSample<f64> for Gamma {
+ fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
+ match self.repr {
+ Small(ref g) => g.ind_sample(rng),
+ One(ref g) => g.ind_sample(rng),
+ Large(ref g) => g.ind_sample(rng),
+ }
+ }
+}
+impl IndependentSample<f64> for GammaSmallShape {
+ fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
+ let Open01(u) = rng.gen::<Open01<f64>>();
+
+ self.large_shape.ind_sample(rng) * u.powf(self.inv_shape)
+ }
+}
+impl IndependentSample<f64> for GammaLargeShape {
+ fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
+ loop {
+ let StandardNormal(x) = rng.gen::<StandardNormal>();
+ let v_cbrt = 1.0 + self.c * x;
+ if v_cbrt <= 0.0 { // a^3 <= 0 iff a <= 0
+ continue
+ }
+
+ let v = v_cbrt * v_cbrt * v_cbrt;
+ let Open01(u) = rng.gen::<Open01<f64>>();
+
+ let x_sqr = x * x;
+ if u < 1.0 - 0.0331 * x_sqr * x_sqr ||
+ u.ln() < 0.5 * x_sqr + self.d * (1.0 - v + v.ln()) {
+ return self.d * v * self.scale
+ }
+ }
+ }
+}
+
+/// The chi-squared distribution `χ²(k)`, where `k` is the degrees of
+/// freedom.
+///
+/// For `k > 0` integral, this distribution is the sum of the squares
+/// of `k` independent standard normal random variables. For other
+/// `k`, this uses the equivalent characterisation
+/// `χ²(k) = Gamma(k/2, 2)`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{ChiSquared, IndependentSample};
+///
+/// let chi = ChiSquared::new(11.0);
+/// let v = chi.ind_sample(&mut rand::thread_rng());
+/// println!("{} is from a χ²(11) distribution", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct ChiSquared {
+ repr: ChiSquaredRepr,
+}
+
+#[derive(Clone, Copy, Debug)]
+enum ChiSquaredRepr {
+ // k == 1, Gamma(alpha, ..) is particularly slow for alpha < 1,
+ // e.g. when alpha = 1/2 as it would be for this case, so special-
+ // casing and using the definition of N(0,1)^2 is faster.
+ DoFExactlyOne,
+ DoFAnythingElse(Gamma),
+}
+
+impl ChiSquared {
+ /// Create a new chi-squared distribution with degrees-of-freedom
+ /// `k`. Panics if `k < 0`.
+ pub fn new(k: f64) -> ChiSquared {
+ let repr = if k == 1.0 {
+ DoFExactlyOne
+ } else {
+ assert!(k > 0.0, "ChiSquared::new called with `k` < 0");
+ DoFAnythingElse(Gamma::new(0.5 * k, 2.0))
+ };
+ ChiSquared { repr: repr }
+ }
+}
+impl Sample<f64> for ChiSquared {
+ fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) }
+}
+impl IndependentSample<f64> for ChiSquared {
+ fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
+ match self.repr {
+ DoFExactlyOne => {
+ // k == 1 => N(0,1)^2
+ let StandardNormal(norm) = rng.gen::<StandardNormal>();
+ norm * norm
+ }
+ DoFAnythingElse(ref g) => g.ind_sample(rng)
+ }
+ }
+}
+
+/// The Fisher F distribution `F(m, n)`.
+///
+/// This distribution is equivalent to the ratio of two normalised
+/// chi-squared distributions, that is, `F(m,n) = (χ²(m)/m) /
+/// (χ²(n)/n)`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{FisherF, IndependentSample};
+///
+/// let f = FisherF::new(2.0, 32.0);
+/// let v = f.ind_sample(&mut rand::thread_rng());
+/// println!("{} is from an F(2, 32) distribution", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct FisherF {
+ numer: ChiSquared,
+ denom: ChiSquared,
+ // denom_dof / numer_dof so that this can just be a straight
+ // multiplication, rather than a division.
+ dof_ratio: f64,
+}
+
+impl FisherF {
+ /// Create a new `FisherF` distribution, with the given
+ /// parameter. Panics if either `m` or `n` are not positive.
+ pub fn new(m: f64, n: f64) -> FisherF {
+ assert!(m > 0.0, "FisherF::new called with `m < 0`");
+ assert!(n > 0.0, "FisherF::new called with `n < 0`");
+
+ FisherF {
+ numer: ChiSquared::new(m),
+ denom: ChiSquared::new(n),
+ dof_ratio: n / m
+ }
+ }
+}
+impl Sample<f64> for FisherF {
+ fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) }
+}
+impl IndependentSample<f64> for FisherF {
+ fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
+ self.numer.ind_sample(rng) / self.denom.ind_sample(rng) * self.dof_ratio
+ }
+}
+
+/// The Student t distribution, `t(nu)`, where `nu` is the degrees of
+/// freedom.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{StudentT, IndependentSample};
+///
+/// let t = StudentT::new(11.0);
+/// let v = t.ind_sample(&mut rand::thread_rng());
+/// println!("{} is from a t(11) distribution", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct StudentT {
+ chi: ChiSquared,
+ dof: f64
+}
+
+impl StudentT {
+ /// Create a new Student t distribution with `n` degrees of
+ /// freedom. Panics if `n <= 0`.
+ pub fn new(n: f64) -> StudentT {
+ assert!(n > 0.0, "StudentT::new called with `n <= 0`");
+ StudentT {
+ chi: ChiSquared::new(n),
+ dof: n
+ }
+ }
+}
+impl Sample<f64> for StudentT {
+ fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) }
+}
+impl IndependentSample<f64> for StudentT {
+ fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
+ let StandardNormal(norm) = rng.gen::<StandardNormal>();
+ norm * (self.dof / self.chi.ind_sample(rng)).sqrt()
+ }
+}
+
+#[cfg(test)]
+mod test {
+ use distributions::{Sample, IndependentSample};
+ use super::{ChiSquared, StudentT, FisherF};
+
+ #[test]
+ fn test_chi_squared_one() {
+ let mut chi = ChiSquared::new(1.0);
+ let mut rng = ::test::rng();
+ for _ in 0..1000 {
+ chi.sample(&mut rng);
+ chi.ind_sample(&mut rng);
+ }
+ }
+ #[test]
+ fn test_chi_squared_small() {
+ let mut chi = ChiSquared::new(0.5);
+ let mut rng = ::test::rng();
+ for _ in 0..1000 {
+ chi.sample(&mut rng);
+ chi.ind_sample(&mut rng);
+ }
+ }
+ #[test]
+ fn test_chi_squared_large() {
+ let mut chi = ChiSquared::new(30.0);
+ let mut rng = ::test::rng();
+ for _ in 0..1000 {
+ chi.sample(&mut rng);
+ chi.ind_sample(&mut rng);
+ }
+ }
+ #[test]
+ #[should_panic]
+ fn test_chi_squared_invalid_dof() {
+ ChiSquared::new(-1.0);
+ }
+
+ #[test]
+ fn test_f() {
+ let mut f = FisherF::new(2.0, 32.0);
+ let mut rng = ::test::rng();
+ for _ in 0..1000 {
+ f.sample(&mut rng);
+ f.ind_sample(&mut rng);
+ }
+ }
+
+ #[test]
+ fn test_t() {
+ let mut t = StudentT::new(11.0);
+ let mut rng = ::test::rng();
+ for _ in 0..1000 {
+ t.sample(&mut rng);
+ t.ind_sample(&mut rng);
+ }
+ }
+}
diff --git a/rand/src/distributions/mod.rs b/rand/src/distributions/mod.rs
new file mode 100644
index 0000000..5de8efb
--- /dev/null
+++ b/rand/src/distributions/mod.rs
@@ -0,0 +1,409 @@
+// 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.
+
+//! Sampling from random distributions.
+//!
+//! This is a generalization of `Rand` to allow parameters to control the
+//! exact properties of the generated values, e.g. the mean and standard
+//! deviation of a normal distribution. The `Sample` trait is the most
+//! general, and allows for generating values that change some state
+//! internally. The `IndependentSample` trait is for generating values
+//! that do not need to record state.
+
+use core::marker;
+
+use {Rng, Rand};
+
+pub use self::range::Range;
+#[cfg(feature="std")]
+pub use self::gamma::{Gamma, ChiSquared, FisherF, StudentT};
+#[cfg(feature="std")]
+pub use self::normal::{Normal, LogNormal};
+#[cfg(feature="std")]
+pub use self::exponential::Exp;
+
+pub mod range;
+#[cfg(feature="std")]
+pub mod gamma;
+#[cfg(feature="std")]
+pub mod normal;
+#[cfg(feature="std")]
+pub mod exponential;
+
+#[cfg(feature="std")]
+mod ziggurat_tables;
+
+/// Types that can be used to create a random instance of `Support`.
+pub trait Sample<Support> {
+ /// Generate a random value of `Support`, using `rng` as the
+ /// source of randomness.
+ fn sample<R: Rng>(&mut self, rng: &mut R) -> Support;
+}
+
+/// `Sample`s that do not require keeping track of state.
+///
+/// Since no state is recorded, each sample is (statistically)
+/// independent of all others, assuming the `Rng` used has this
+/// property.
+// FIXME maybe having this separate is overkill (the only reason is to
+// take &self rather than &mut self)? or maybe this should be the
+// trait called `Sample` and the other should be `DependentSample`.
+pub trait IndependentSample<Support>: Sample<Support> {
+ /// Generate a random value.
+ fn ind_sample<R: Rng>(&self, &mut R) -> Support;
+}
+
+/// A wrapper for generating types that implement `Rand` via the
+/// `Sample` & `IndependentSample` traits.
+#[derive(Debug)]
+pub struct RandSample<Sup> {
+ _marker: marker::PhantomData<fn() -> Sup>,
+}
+
+impl<Sup> Copy for RandSample<Sup> {}
+impl<Sup> Clone for RandSample<Sup> {
+ fn clone(&self) -> Self { *self }
+}
+
+impl<Sup: Rand> Sample<Sup> for RandSample<Sup> {
+ fn sample<R: Rng>(&mut self, rng: &mut R) -> Sup { self.ind_sample(rng) }
+}
+
+impl<Sup: Rand> IndependentSample<Sup> for RandSample<Sup> {
+ fn ind_sample<R: Rng>(&self, rng: &mut R) -> Sup {
+ rng.gen()
+ }
+}
+
+impl<Sup> RandSample<Sup> {
+ pub fn new() -> RandSample<Sup> {
+ RandSample { _marker: marker::PhantomData }
+ }
+}
+
+/// A value with a particular weight for use with `WeightedChoice`.
+#[derive(Copy, Clone, Debug)]
+pub struct Weighted<T> {
+ /// The numerical weight of this item
+ pub weight: u32,
+ /// The actual item which is being weighted
+ pub item: T,
+}
+
+/// A distribution that selects from a finite collection of weighted items.
+///
+/// Each item has an associated weight that influences how likely it
+/// is to be chosen: higher weight is more likely.
+///
+/// The `Clone` restriction is a limitation of the `Sample` and
+/// `IndependentSample` traits. Note that `&T` is (cheaply) `Clone` for
+/// all `T`, as is `u32`, so one can store references or indices into
+/// another vector.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{Weighted, WeightedChoice, IndependentSample};
+///
+/// let mut items = vec!(Weighted { weight: 2, item: 'a' },
+/// Weighted { weight: 4, item: 'b' },
+/// Weighted { weight: 1, item: 'c' });
+/// let wc = WeightedChoice::new(&mut items);
+/// let mut rng = rand::thread_rng();
+/// for _ in 0..16 {
+/// // on average prints 'a' 4 times, 'b' 8 and 'c' twice.
+/// println!("{}", wc.ind_sample(&mut rng));
+/// }
+/// ```
+#[derive(Debug)]
+pub struct WeightedChoice<'a, T:'a> {
+ items: &'a mut [Weighted<T>],
+ weight_range: Range<u32>
+}
+
+impl<'a, T: Clone> WeightedChoice<'a, T> {
+ /// Create a new `WeightedChoice`.
+ ///
+ /// Panics if:
+ ///
+ /// - `items` is empty
+ /// - the total weight is 0
+ /// - the total weight is larger than a `u32` can contain.
+ pub fn new(items: &'a mut [Weighted<T>]) -> WeightedChoice<'a, T> {
+ // strictly speaking, this is subsumed by the total weight == 0 case
+ assert!(!items.is_empty(), "WeightedChoice::new called with no items");
+
+ let mut running_total: u32 = 0;
+
+ // we convert the list from individual weights to cumulative
+ // weights so we can binary search. This *could* drop elements
+ // with weight == 0 as an optimisation.
+ for item in items.iter_mut() {
+ running_total = match running_total.checked_add(item.weight) {
+ Some(n) => n,
+ None => panic!("WeightedChoice::new called with a total weight \
+ larger than a u32 can contain")
+ };
+
+ item.weight = running_total;
+ }
+ assert!(running_total != 0, "WeightedChoice::new called with a total weight of 0");
+
+ WeightedChoice {
+ items: items,
+ // we're likely to be generating numbers in this range
+ // relatively often, so might as well cache it
+ weight_range: Range::new(0, running_total)
+ }
+ }
+}
+
+impl<'a, T: Clone> Sample<T> for WeightedChoice<'a, T> {
+ fn sample<R: Rng>(&mut self, rng: &mut R) -> T { self.ind_sample(rng) }
+}
+
+impl<'a, T: Clone> IndependentSample<T> for WeightedChoice<'a, T> {
+ fn ind_sample<R: Rng>(&self, rng: &mut R) -> T {
+ // we want to find the first element that has cumulative
+ // weight > sample_weight, which we do by binary since the
+ // cumulative weights of self.items are sorted.
+
+ // choose a weight in [0, total_weight)
+ let sample_weight = self.weight_range.ind_sample(rng);
+
+ // short circuit when it's the first item
+ if sample_weight < self.items[0].weight {
+ return self.items[0].item.clone();
+ }
+
+ let mut idx = 0;
+ let mut modifier = self.items.len();
+
+ // now we know that every possibility has an element to the
+ // left, so we can just search for the last element that has
+ // cumulative weight <= sample_weight, then the next one will
+ // be "it". (Note that this greatest element will never be the
+ // last element of the vector, since sample_weight is chosen
+ // in [0, total_weight) and the cumulative weight of the last
+ // one is exactly the total weight.)
+ while modifier > 1 {
+ let i = idx + modifier / 2;
+ if self.items[i].weight <= sample_weight {
+ // we're small, so look to the right, but allow this
+ // exact element still.
+ idx = i;
+ // we need the `/ 2` to round up otherwise we'll drop
+ // the trailing elements when `modifier` is odd.
+ modifier += 1;
+ } else {
+ // otherwise we're too big, so go left. (i.e. do
+ // nothing)
+ }
+ modifier /= 2;
+ }
+ return self.items[idx + 1].item.clone();
+ }
+}
+
+/// Sample a random number using the Ziggurat method (specifically the
+/// ZIGNOR variant from Doornik 2005). Most of the arguments are
+/// directly from the paper:
+///
+/// * `rng`: source of randomness
+/// * `symmetric`: whether this is a symmetric distribution, or one-sided with P(x < 0) = 0.
+/// * `X`: the $x_i$ abscissae.
+/// * `F`: precomputed values of the PDF at the $x_i$, (i.e. $f(x_i)$)
+/// * `F_DIFF`: precomputed values of $f(x_i) - f(x_{i+1})$
+/// * `pdf`: the probability density function
+/// * `zero_case`: manual sampling from the tail when we chose the
+/// bottom box (i.e. i == 0)
+
+// the perf improvement (25-50%) is definitely worth the extra code
+// size from force-inlining.
+#[cfg(feature="std")]
+#[inline(always)]
+fn ziggurat<R: Rng, P, Z>(
+ rng: &mut R,
+ symmetric: bool,
+ x_tab: ziggurat_tables::ZigTable,
+ f_tab: ziggurat_tables::ZigTable,
+ mut pdf: P,
+ mut zero_case: Z)
+ -> f64 where P: FnMut(f64) -> f64, Z: FnMut(&mut R, f64) -> f64 {
+ const SCALE: f64 = (1u64 << 53) as f64;
+ loop {
+ // reimplement the f64 generation as an optimisation suggested
+ // by the Doornik paper: we have a lot of precision-space
+ // (i.e. there are 11 bits of the 64 of a u64 to use after
+ // creating a f64), so we might as well reuse some to save
+ // generating a whole extra random number. (Seems to be 15%
+ // faster.)
+ //
+ // This unfortunately misses out on the benefits of direct
+ // floating point generation if an RNG like dSMFT is
+ // used. (That is, such RNGs create floats directly, highly
+ // efficiently and overload next_f32/f64, so by not calling it
+ // this may be slower than it would be otherwise.)
+ // FIXME: investigate/optimise for the above.
+ let bits: u64 = rng.gen();
+ let i = (bits & 0xff) as usize;
+ let f = (bits >> 11) as f64 / SCALE;
+
+ // u is either U(-1, 1) or U(0, 1) depending on if this is a
+ // symmetric distribution or not.
+ let u = if symmetric {2.0 * f - 1.0} else {f};
+ let x = u * x_tab[i];
+
+ let test_x = if symmetric { x.abs() } else {x};
+
+ // algebraically equivalent to |u| < x_tab[i+1]/x_tab[i] (or u < x_tab[i+1]/x_tab[i])
+ if test_x < x_tab[i + 1] {
+ return x;
+ }
+ if i == 0 {
+ return zero_case(rng, u);
+ }
+ // algebraically equivalent to f1 + DRanU()*(f0 - f1) < 1
+ if f_tab[i + 1] + (f_tab[i] - f_tab[i + 1]) * rng.gen::<f64>() < pdf(x) {
+ return x;
+ }
+ }
+}
+
+#[cfg(test)]
+mod tests {
+
+ use {Rng, Rand};
+ use super::{RandSample, WeightedChoice, Weighted, Sample, IndependentSample};
+
+ #[derive(PartialEq, Debug)]
+ struct ConstRand(usize);
+ impl Rand for ConstRand {
+ fn rand<R: Rng>(_: &mut R) -> ConstRand {
+ ConstRand(0)
+ }
+ }
+
+ // 0, 1, 2, 3, ...
+ struct CountingRng { i: u32 }
+ impl Rng for CountingRng {
+ fn next_u32(&mut self) -> u32 {
+ self.i += 1;
+ self.i - 1
+ }
+ fn next_u64(&mut self) -> u64 {
+ self.next_u32() as u64
+ }
+ }
+
+ #[test]
+ fn test_rand_sample() {
+ let mut rand_sample = RandSample::<ConstRand>::new();
+
+ assert_eq!(rand_sample.sample(&mut ::test::rng()), ConstRand(0));
+ assert_eq!(rand_sample.ind_sample(&mut ::test::rng()), ConstRand(0));
+ }
+ #[test]
+ fn test_weighted_choice() {
+ // this makes assumptions about the internal implementation of
+ // WeightedChoice, specifically: it doesn't reorder the items,
+ // it doesn't do weird things to the RNG (so 0 maps to 0, 1 to
+ // 1, internally; modulo a modulo operation).
+
+ macro_rules! t {
+ ($items:expr, $expected:expr) => {{
+ let mut items = $items;
+ let wc = WeightedChoice::new(&mut items);
+ let expected = $expected;
+
+ let mut rng = CountingRng { i: 0 };
+
+ for &val in expected.iter() {
+ assert_eq!(wc.ind_sample(&mut rng), val)
+ }
+ }}
+ }
+
+ t!(vec!(Weighted { weight: 1, item: 10}), [10]);
+
+ // skip some
+ t!(vec!(Weighted { weight: 0, item: 20},
+ Weighted { weight: 2, item: 21},
+ Weighted { weight: 0, item: 22},
+ Weighted { weight: 1, item: 23}),
+ [21,21, 23]);
+
+ // different weights
+ t!(vec!(Weighted { weight: 4, item: 30},
+ Weighted { weight: 3, item: 31}),
+ [30,30,30,30, 31,31,31]);
+
+ // check that we're binary searching
+ // correctly with some vectors of odd
+ // length.
+ t!(vec!(Weighted { weight: 1, item: 40},
+ Weighted { weight: 1, item: 41},
+ Weighted { weight: 1, item: 42},
+ Weighted { weight: 1, item: 43},
+ Weighted { weight: 1, item: 44}),
+ [40, 41, 42, 43, 44]);
+ t!(vec!(Weighted { weight: 1, item: 50},
+ Weighted { weight: 1, item: 51},
+ Weighted { weight: 1, item: 52},
+ Weighted { weight: 1, item: 53},
+ Weighted { weight: 1, item: 54},
+ Weighted { weight: 1, item: 55},
+ Weighted { weight: 1, item: 56}),
+ [50, 51, 52, 53, 54, 55, 56]);
+ }
+
+ #[test]
+ fn test_weighted_clone_initialization() {
+ let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
+ let clone = initial.clone();
+ assert_eq!(initial.weight, clone.weight);
+ assert_eq!(initial.item, clone.item);
+ }
+
+ #[test] #[should_panic]
+ fn test_weighted_clone_change_weight() {
+ let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
+ let mut clone = initial.clone();
+ clone.weight = 5;
+ assert_eq!(initial.weight, clone.weight);
+ }
+
+ #[test] #[should_panic]
+ fn test_weighted_clone_change_item() {
+ let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
+ let mut clone = initial.clone();
+ clone.item = 5;
+ assert_eq!(initial.item, clone.item);
+
+ }
+
+ #[test] #[should_panic]
+ fn test_weighted_choice_no_items() {
+ WeightedChoice::<isize>::new(&mut []);
+ }
+ #[test] #[should_panic]
+ fn test_weighted_choice_zero_weight() {
+ WeightedChoice::new(&mut [Weighted { weight: 0, item: 0},
+ Weighted { weight: 0, item: 1}]);
+ }
+ #[test] #[should_panic]
+ fn test_weighted_choice_weight_overflows() {
+ let x = ::std::u32::MAX / 2; // x + x + 2 is the overflow
+ WeightedChoice::new(&mut [Weighted { weight: x, item: 0 },
+ Weighted { weight: 1, item: 1 },
+ Weighted { weight: x, item: 2 },
+ Weighted { weight: 1, item: 3 }]);
+ }
+}
diff --git a/rand/src/distributions/normal.rs b/rand/src/distributions/normal.rs
new file mode 100644
index 0000000..280613d
--- /dev/null
+++ b/rand/src/distributions/normal.rs
@@ -0,0 +1,201 @@
+// 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 and derived distributions.
+
+use {Rng, Rand, Open01};
+use 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.
+///
+/// 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
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::normal::StandardNormal;
+///
+/// let StandardNormal(x) = rand::random();
+/// println!("{}", x);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct StandardNormal(pub f64);
+
+impl Rand for StandardNormal {
+ fn rand<R:Rng>(rng: &mut R) -> StandardNormal {
+ #[inline]
+ fn pdf(x: f64) -> f64 {
+ (-x*x/2.0).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 Open01(x_) = rng.gen::<Open01<f64>>();
+ let Open01(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 rand::distributions::{Normal, IndependentSample};
+///
+/// // mean 2, standard deviation 3
+/// let normal = Normal::new(2.0, 3.0);
+/// let v = normal.ind_sample(&mut rand::thread_rng());
+/// println!("{} is from a N(2, 9) distribution", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Normal {
+ mean: f64,
+ std_dev: f64,
+}
+
+impl Normal {
+ /// Construct a new `Normal` distribution with the given mean and
+ /// standard deviation.
+ ///
+ /// # Panics
+ ///
+ /// Panics if `std_dev < 0`.
+ #[inline]
+ 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 {
+ let StandardNormal(n) = rng.gen::<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
+///
+/// ```rust
+/// use rand::distributions::{LogNormal, IndependentSample};
+///
+/// // mean 2, standard deviation 3
+/// let log_normal = LogNormal::new(2.0, 3.0);
+/// let v = log_normal.ind_sample(&mut rand::thread_rng());
+/// println!("{} is from an ln N(2, 9) distribution", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct LogNormal {
+ norm: Normal
+}
+
+impl LogNormal {
+ /// Construct a new `LogNormal` distribution with the given mean
+ /// and standard deviation.
+ ///
+ /// # Panics
+ ///
+ /// Panics if `std_dev < 0`.
+ #[inline]
+ pub fn new(mean: f64, std_dev: f64) -> LogNormal {
+ assert!(std_dev >= 0.0, "LogNormal::new called with `std_dev` < 0");
+ 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()
+ }
+}
+
+#[cfg(test)]
+mod tests {
+ use distributions::{Sample, IndependentSample};
+ use super::{Normal, LogNormal};
+
+ #[test]
+ fn test_normal() {
+ let mut norm = Normal::new(10.0, 10.0);
+ let mut rng = ::test::rng();
+ for _ in 0..1000 {
+ norm.sample(&mut rng);
+ norm.ind_sample(&mut rng);
+ }
+ }
+ #[test]
+ #[should_panic]
+ fn test_normal_invalid_sd() {
+ Normal::new(10.0, -1.0);
+ }
+
+
+ #[test]
+ fn test_log_normal() {
+ let mut lnorm = LogNormal::new(10.0, 10.0);
+ let mut rng = ::test::rng();
+ for _ in 0..1000 {
+ lnorm.sample(&mut rng);
+ lnorm.ind_sample(&mut rng);
+ }
+ }
+ #[test]
+ #[should_panic]
+ fn test_log_normal_invalid_sd() {
+ LogNormal::new(10.0, -1.0);
+ }
+}
diff --git a/rand/src/distributions/range.rs b/rand/src/distributions/range.rs
new file mode 100644
index 0000000..935a00a
--- /dev/null
+++ b/rand/src/distributions/range.rs
@@ -0,0 +1,241 @@
+// 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.
+
+//! Generating numbers between two others.
+
+// this is surprisingly complicated to be both generic & correct
+
+use core::num::Wrapping as w;
+
+use Rng;
+use distributions::{Sample, IndependentSample};
+
+/// Sample values uniformly between two bounds.
+///
+/// This gives a uniform distribution (assuming the RNG used to sample
+/// it is itself uniform & the `SampleRange` implementation for the
+/// given type is correct), even for edge cases like `low = 0u8`,
+/// `high = 170u8`, for which a naive modulo operation would return
+/// numbers less than 85 with double the probability to those greater
+/// than 85.
+///
+/// Types should attempt to sample in `[low, high)`, i.e., not
+/// including `high`, but this may be very difficult. All the
+/// primitive integer types satisfy this property, and the float types
+/// normally satisfy it, but rounding may mean `high` can occur.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{IndependentSample, Range};
+///
+/// fn main() {
+/// let between = Range::new(10, 10000);
+/// let mut rng = rand::thread_rng();
+/// let mut sum = 0;
+/// for _ in 0..1000 {
+/// sum += between.ind_sample(&mut rng);
+/// }
+/// println!("{}", sum);
+/// }
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Range<X> {
+ low: X,
+ range: X,
+ accept_zone: X
+}
+
+impl<X: SampleRange + PartialOrd> Range<X> {
+ /// Create a new `Range` instance that samples uniformly from
+ /// `[low, high)`. Panics if `low >= high`.
+ pub fn new(low: X, high: X) -> Range<X> {
+ assert!(low < high, "Range::new called with `low >= high`");
+ SampleRange::construct_range(low, high)
+ }
+}
+
+impl<Sup: SampleRange> Sample<Sup> for Range<Sup> {
+ #[inline]
+ fn sample<R: Rng>(&mut self, rng: &mut R) -> Sup { self.ind_sample(rng) }
+}
+impl<Sup: SampleRange> IndependentSample<Sup> for Range<Sup> {
+ fn ind_sample<R: Rng>(&self, rng: &mut R) -> Sup {
+ SampleRange::sample_range(self, rng)
+ }
+}
+
+/// The helper trait for types that have a sensible way to sample
+/// uniformly between two values. This should not be used directly,
+/// and is only to facilitate `Range`.
+pub trait SampleRange : Sized {
+ /// Construct the `Range` object that `sample_range`
+ /// requires. This should not ever be called directly, only via
+ /// `Range::new`, which will check that `low < high`, so this
+ /// function doesn't have to repeat the check.
+ fn construct_range(low: Self, high: Self) -> Range<Self>;
+
+ /// Sample a value from the given `Range` with the given `Rng` as
+ /// a source of randomness.
+ fn sample_range<R: Rng>(r: &Range<Self>, rng: &mut R) -> Self;
+}
+
+macro_rules! integer_impl {
+ ($ty:ty, $unsigned:ident) => {
+ impl SampleRange for $ty {
+ // we play free and fast with unsigned vs signed here
+ // (when $ty is signed), but that's fine, since the
+ // contract of this macro is for $ty and $unsigned to be
+ // "bit-equal", so casting between them is a no-op & a
+ // bijection.
+
+ #[inline]
+ fn construct_range(low: $ty, high: $ty) -> Range<$ty> {
+ let range = (w(high as $unsigned) - w(low as $unsigned)).0;
+ let unsigned_max: $unsigned = ::core::$unsigned::MAX;
+
+ // this is the largest number that fits into $unsigned
+ // that `range` divides evenly, so, if we've sampled
+ // `n` uniformly from this region, then `n % range` is
+ // uniform in [0, range)
+ let zone = unsigned_max - unsigned_max % range;
+
+ Range {
+ low: low,
+ range: range as $ty,
+ accept_zone: zone as $ty
+ }
+ }
+
+ #[inline]
+ fn sample_range<R: Rng>(r: &Range<$ty>, rng: &mut R) -> $ty {
+ loop {
+ // rejection sample
+ let v = rng.gen::<$unsigned>();
+ // until we find something that fits into the
+ // region which r.range evenly divides (this will
+ // be uniformly distributed)
+ if v < r.accept_zone as $unsigned {
+ // and return it, with some adjustments
+ return (w(r.low) + w((v % r.range as $unsigned) as $ty)).0;
+ }
+ }
+ }
+ }
+ }
+}
+
+integer_impl! { i8, u8 }
+integer_impl! { i16, u16 }
+integer_impl! { i32, u32 }
+integer_impl! { i64, u64 }
+#[cfg(feature = "i128_support")]
+integer_impl! { i128, u128 }
+integer_impl! { isize, usize }
+integer_impl! { u8, u8 }
+integer_impl! { u16, u16 }
+integer_impl! { u32, u32 }
+integer_impl! { u64, u64 }
+#[cfg(feature = "i128_support")]
+integer_impl! { u128, u128 }
+integer_impl! { usize, usize }
+
+macro_rules! float_impl {
+ ($ty:ty) => {
+ impl SampleRange for $ty {
+ fn construct_range(low: $ty, high: $ty) -> Range<$ty> {
+ Range {
+ low: low,
+ range: high - low,
+ accept_zone: 0.0 // unused
+ }
+ }
+ fn sample_range<R: Rng>(r: &Range<$ty>, rng: &mut R) -> $ty {
+ r.low + r.range * rng.gen::<$ty>()
+ }
+ }
+ }
+}
+
+float_impl! { f32 }
+float_impl! { f64 }
+
+#[cfg(test)]
+mod tests {
+ use distributions::{Sample, IndependentSample};
+ use super::Range as Range;
+
+ #[should_panic]
+ #[test]
+ fn test_range_bad_limits_equal() {
+ Range::new(10, 10);
+ }
+ #[should_panic]
+ #[test]
+ fn test_range_bad_limits_flipped() {
+ Range::new(10, 5);
+ }
+
+ #[test]
+ fn test_integers() {
+ let mut rng = ::test::rng();
+ macro_rules! t {
+ ($($ty:ident),*) => {{
+ $(
+ let v: &[($ty, $ty)] = &[(0, 10),
+ (10, 127),
+ (::core::$ty::MIN, ::core::$ty::MAX)];
+ for &(low, high) in v.iter() {
+ let mut sampler: Range<$ty> = Range::new(low, high);
+ for _ in 0..1000 {
+ let v = sampler.sample(&mut rng);
+ assert!(low <= v && v < high);
+ let v = sampler.ind_sample(&mut rng);
+ assert!(low <= v && v < high);
+ }
+ }
+ )*
+ }}
+ }
+ #[cfg(not(feature = "i128_support"))]
+ t!(i8, i16, i32, i64, isize,
+ u8, u16, u32, u64, usize);
+ #[cfg(feature = "i128_support")]
+ t!(i8, i16, i32, i64, i128, isize,
+ u8, u16, u32, u64, u128, usize);
+ }
+
+ #[test]
+ fn test_floats() {
+ let mut rng = ::test::rng();
+ macro_rules! t {
+ ($($ty:ty),*) => {{
+ $(
+ let v: &[($ty, $ty)] = &[(0.0, 100.0),
+ (-1e35, -1e25),
+ (1e-35, 1e-25),
+ (-1e35, 1e35)];
+ for &(low, high) in v.iter() {
+ let mut sampler: Range<$ty> = Range::new(low, high);
+ for _ in 0..1000 {
+ let v = sampler.sample(&mut rng);
+ assert!(low <= v && v < high);
+ let v = sampler.ind_sample(&mut rng);
+ assert!(low <= v && v < high);
+ }
+ }
+ )*
+ }}
+ }
+
+ t!(f32, f64)
+ }
+
+}
diff --git a/rand/src/distributions/ziggurat_tables.rs b/rand/src/distributions/ziggurat_tables.rs
new file mode 100644
index 0000000..b6de4bf
--- /dev/null
+++ b/rand/src/distributions/ziggurat_tables.rs
@@ -0,0 +1,280 @@
+// 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.
+
+// Tables for distributions which are sampled using the ziggurat
+// algorithm. Autogenerated by `ziggurat_tables.py`.
+
+pub type ZigTable = &'static [f64; 257];
+pub const ZIG_NORM_R: f64 = 3.654152885361008796;
+pub static ZIG_NORM_X: [f64; 257] =
+ [3.910757959537090045, 3.654152885361008796, 3.449278298560964462, 3.320244733839166074,
+ 3.224575052047029100, 3.147889289517149969, 3.083526132001233044, 3.027837791768635434,
+ 2.978603279880844834, 2.934366867207854224, 2.894121053612348060, 2.857138730872132548,
+ 2.822877396825325125, 2.790921174000785765, 2.760944005278822555, 2.732685359042827056,
+ 2.705933656121858100, 2.680514643284522158, 2.656283037575502437, 2.633116393630324570,
+ 2.610910518487548515, 2.589575986706995181, 2.569035452680536569, 2.549221550323460761,
+ 2.530075232158516929, 2.511544441625342294, 2.493583041269680667, 2.476149939669143318,
+ 2.459208374333311298, 2.442725318198956774, 2.426670984935725972, 2.411018413899685520,
+ 2.395743119780480601, 2.380822795170626005, 2.366237056715818632, 2.351967227377659952,
+ 2.337996148795031370, 2.324308018869623016, 2.310888250599850036, 2.297723348901329565,
+ 2.284800802722946056, 2.272108990226823888, 2.259637095172217780, 2.247375032945807760,
+ 2.235313384928327984, 2.223443340090905718, 2.211756642882544366, 2.200245546609647995,
+ 2.188902771624720689, 2.177721467738641614, 2.166695180352645966, 2.155817819875063268,
+ 2.145083634046203613, 2.134487182844320152, 2.124023315687815661, 2.113687150684933957,
+ 2.103474055713146829, 2.093379631137050279, 2.083399693996551783, 2.073530263516978778,
+ 2.063767547809956415, 2.054107931648864849, 2.044547965215732788, 2.035084353727808715,
+ 2.025713947862032960, 2.016433734904371722, 2.007240830558684852, 1.998132471356564244,
+ 1.989106007615571325, 1.980158896898598364, 1.971288697931769640, 1.962493064942461896,
+ 1.953769742382734043, 1.945116560006753925, 1.936531428273758904, 1.928012334050718257,
+ 1.919557336591228847, 1.911164563769282232, 1.902832208548446369, 1.894558525668710081,
+ 1.886341828534776388, 1.878180486290977669, 1.870072921069236838, 1.862017605397632281,
+ 1.854013059758148119, 1.846057850283119750, 1.838150586580728607, 1.830289919680666566,
+ 1.822474540091783224, 1.814703175964167636, 1.806974591348693426, 1.799287584547580199,
+ 1.791640986550010028, 1.784033659547276329, 1.776464495522344977, 1.768932414909077933,
+ 1.761436365316706665, 1.753975320315455111, 1.746548278279492994, 1.739154261283669012,
+ 1.731792314050707216, 1.724461502945775715, 1.717160915015540690, 1.709889657069006086,
+ 1.702646854797613907, 1.695431651932238548, 1.688243209434858727, 1.681080704722823338,
+ 1.673943330923760353, 1.666830296159286684, 1.659740822855789499, 1.652674147080648526,
+ 1.645629517902360339, 1.638606196773111146, 1.631603456932422036, 1.624620582830568427,
+ 1.617656869570534228, 1.610711622367333673, 1.603784156023583041, 1.596873794420261339,
+ 1.589979870021648534, 1.583101723393471438, 1.576238702733332886, 1.569390163412534456,
+ 1.562555467528439657, 1.555733983466554893, 1.548925085471535512, 1.542128153226347553,
+ 1.535342571438843118, 1.528567729435024614, 1.521803020758293101, 1.515047842773992404,
+ 1.508301596278571965, 1.501563685112706548, 1.494833515777718391, 1.488110497054654369,
+ 1.481394039625375747, 1.474683555695025516, 1.467978458615230908, 1.461278162507407830,
+ 1.454582081885523293, 1.447889631277669675, 1.441200224845798017, 1.434513276002946425,
+ 1.427828197027290358, 1.421144398672323117, 1.414461289772464658, 1.407778276843371534,
+ 1.401094763676202559, 1.394410150925071257, 1.387723835686884621, 1.381035211072741964,
+ 1.374343665770030531, 1.367648583594317957, 1.360949343030101844, 1.354245316759430606,
+ 1.347535871177359290, 1.340820365893152122, 1.334098153216083604, 1.327368577624624679,
+ 1.320630975217730096, 1.313884673146868964, 1.307128989027353860, 1.300363230327433728,
+ 1.293586693733517645, 1.286798664489786415, 1.279998415710333237, 1.273185207661843732,
+ 1.266358287014688333, 1.259516886060144225, 1.252660221891297887, 1.245787495544997903,
+ 1.238897891102027415, 1.231990574742445110, 1.225064693752808020, 1.218119375481726552,
+ 1.211153726239911244, 1.204166830140560140, 1.197157747875585931, 1.190125515422801650,
+ 1.183069142678760732, 1.175987612011489825, 1.168879876726833800, 1.161744859441574240,
+ 1.154581450355851802, 1.147388505416733873, 1.140164844363995789, 1.132909248648336975,
+ 1.125620459211294389, 1.118297174115062909, 1.110938046009249502, 1.103541679420268151,
+ 1.096106627847603487, 1.088631390649514197, 1.081114409698889389, 1.073554065787871714,
+ 1.065948674757506653, 1.058296483326006454, 1.050595664586207123, 1.042844313139370538,
+ 1.035040439828605274, 1.027181966030751292, 1.019266717460529215, 1.011292417434978441,
+ 1.003256679539591412, 0.995156999629943084, 0.986990747093846266, 0.978755155288937750,
+ 0.970447311058864615, 0.962064143217605250, 0.953602409875572654, 0.945058684462571130,
+ 0.936429340280896860, 0.927710533396234771, 0.918898183643734989, 0.909987953490768997,
+ 0.900975224455174528, 0.891855070726792376, 0.882622229578910122, 0.873271068082494550,
+ 0.863795545546826915, 0.854189171001560554, 0.844444954902423661, 0.834555354079518752,
+ 0.824512208745288633, 0.814306670128064347, 0.803929116982664893, 0.793369058833152785,
+ 0.782615023299588763, 0.771654424216739354, 0.760473406422083165, 0.749056662009581653,
+ 0.737387211425838629, 0.725446140901303549, 0.713212285182022732, 0.700661841097584448,
+ 0.687767892786257717, 0.674499822827436479, 0.660822574234205984, 0.646695714884388928,
+ 0.632072236375024632, 0.616896989996235545, 0.601104617743940417, 0.584616766093722262,
+ 0.567338257040473026, 0.549151702313026790, 0.529909720646495108, 0.509423329585933393,
+ 0.487443966121754335, 0.463634336771763245, 0.437518402186662658, 0.408389134588000746,
+ 0.375121332850465727, 0.335737519180459465, 0.286174591747260509, 0.215241895913273806,
+ 0.000000000000000000];
+pub static ZIG_NORM_F: [f64; 257] =
+ [0.000477467764586655, 0.001260285930498598, 0.002609072746106363, 0.004037972593371872,
+ 0.005522403299264754, 0.007050875471392110, 0.008616582769422917, 0.010214971439731100,
+ 0.011842757857943104, 0.013497450601780807, 0.015177088307982072, 0.016880083152595839,
+ 0.018605121275783350, 0.020351096230109354, 0.022117062707379922, 0.023902203305873237,
+ 0.025705804008632656, 0.027527235669693315, 0.029365939758230111, 0.031221417192023690,
+ 0.033093219458688698, 0.034980941461833073, 0.036884215688691151, 0.038802707404656918,
+ 0.040736110656078753, 0.042684144916619378, 0.044646552251446536, 0.046623094902089664,
+ 0.048613553216035145, 0.050617723861121788, 0.052635418276973649, 0.054666461325077916,
+ 0.056710690106399467, 0.058767952921137984, 0.060838108349751806, 0.062921024437977854,
+ 0.065016577971470438, 0.067124653828023989, 0.069245144397250269, 0.071377949059141965,
+ 0.073522973714240991, 0.075680130359194964, 0.077849336702372207, 0.080030515814947509,
+ 0.082223595813495684, 0.084428509570654661, 0.086645194450867782, 0.088873592068594229,
+ 0.091113648066700734, 0.093365311913026619, 0.095628536713353335, 0.097903279039215627,
+ 0.100189498769172020, 0.102487158942306270, 0.104796225622867056, 0.107116667775072880,
+ 0.109448457147210021, 0.111791568164245583, 0.114145977828255210, 0.116511665626037014,
+ 0.118888613443345698, 0.121276805485235437, 0.123676228202051403, 0.126086870220650349,
+ 0.128508722280473636, 0.130941777174128166, 0.133386029692162844, 0.135841476571757352,
+ 0.138308116449064322, 0.140785949814968309, 0.143274978974047118, 0.145775208006537926,
+ 0.148286642733128721, 0.150809290682410169, 0.153343161060837674, 0.155888264725064563,
+ 0.158444614156520225, 0.161012223438117663, 0.163591108232982951, 0.166181285765110071,
+ 0.168782774801850333, 0.171395595638155623, 0.174019770082499359, 0.176655321444406654,
+ 0.179302274523530397, 0.181960655600216487, 0.184630492427504539, 0.187311814224516926,
+ 0.190004651671193070, 0.192709036904328807, 0.195425003514885592, 0.198152586546538112,
+ 0.200891822495431333, 0.203642749311121501, 0.206405406398679298, 0.209179834621935651,
+ 0.211966076307852941, 0.214764175252008499, 0.217574176725178370, 0.220396127481011589,
+ 0.223230075764789593, 0.226076071323264877, 0.228934165415577484, 0.231804410825248525,
+ 0.234686861873252689, 0.237581574432173676, 0.240488605941449107, 0.243408015423711988,
+ 0.246339863502238771, 0.249284212419516704, 0.252241126056943765, 0.255210669955677150,
+ 0.258192911338648023, 0.261187919133763713, 0.264195763998317568, 0.267216518344631837,
+ 0.270250256366959984, 0.273297054069675804, 0.276356989296781264, 0.279430141762765316,
+ 0.282516593084849388, 0.285616426816658109, 0.288729728483353931, 0.291856585618280984,
+ 0.294997087801162572, 0.298151326697901342, 0.301319396102034120, 0.304501391977896274,
+ 0.307697412505553769, 0.310907558127563710, 0.314131931597630143, 0.317370638031222396,
+ 0.320623784958230129, 0.323891482377732021, 0.327173842814958593, 0.330470981380537099,
+ 0.333783015832108509, 0.337110066638412809, 0.340452257045945450, 0.343809713148291340,
+ 0.347182563958251478, 0.350570941482881204, 0.353974980801569250, 0.357394820147290515,
+ 0.360830600991175754, 0.364282468130549597, 0.367750569780596226, 0.371235057669821344,
+ 0.374736087139491414, 0.378253817247238111, 0.381788410875031348, 0.385340034841733958,
+ 0.388908860020464597, 0.392495061461010764, 0.396098818517547080, 0.399720314981931668,
+ 0.403359739222868885, 0.407017284331247953, 0.410693148271983222, 0.414387534042706784,
+ 0.418100649839684591, 0.421832709231353298, 0.425583931339900579, 0.429354541031341519,
+ 0.433144769114574058, 0.436954852549929273, 0.440785034667769915, 0.444635565397727750,
+ 0.448506701509214067, 0.452398706863882505, 0.456311852680773566, 0.460246417814923481,
+ 0.464202689050278838, 0.468180961407822172, 0.472181538469883255, 0.476204732721683788,
+ 0.480250865911249714, 0.484320269428911598, 0.488413284707712059, 0.492530263646148658,
+ 0.496671569054796314, 0.500837575128482149, 0.505028667945828791, 0.509245245998136142,
+ 0.513487720749743026, 0.517756517232200619, 0.522052074674794864, 0.526374847174186700,
+ 0.530725304406193921, 0.535103932383019565, 0.539511234259544614, 0.543947731192649941,
+ 0.548413963257921133, 0.552910490428519918, 0.557437893621486324, 0.561996775817277916,
+ 0.566587763258951771, 0.571211506738074970, 0.575868682975210544, 0.580559996103683473,
+ 0.585286179266300333, 0.590047996335791969, 0.594846243770991268, 0.599681752622167719,
+ 0.604555390700549533, 0.609468064928895381, 0.614420723892076803, 0.619414360609039205,
+ 0.624450015550274240, 0.629528779928128279, 0.634651799290960050, 0.639820277456438991,
+ 0.645035480824251883, 0.650298743114294586, 0.655611470583224665, 0.660975147780241357,
+ 0.666391343912380640, 0.671861719900766374, 0.677388036222513090, 0.682972161648791376,
+ 0.688616083008527058, 0.694321916130032579, 0.700091918140490099, 0.705928501336797409,
+ 0.711834248882358467, 0.717811932634901395, 0.723864533472881599, 0.729995264565802437,
+ 0.736207598131266683, 0.742505296344636245, 0.748892447223726720, 0.755373506511754500,
+ 0.761953346841546475, 0.768637315803334831, 0.775431304986138326, 0.782341832659861902,
+ 0.789376143571198563, 0.796542330428254619, 0.803849483176389490, 0.811307874318219935,
+ 0.818929191609414797, 0.826726833952094231, 0.834716292992930375, 0.842915653118441077,
+ 0.851346258465123684, 0.860033621203008636, 0.869008688043793165, 0.878309655816146839,
+ 0.887984660763399880, 0.898095921906304051, 0.908726440060562912, 0.919991505048360247,
+ 0.932060075968990209, 0.945198953453078028, 0.959879091812415930, 0.977101701282731328,
+ 1.000000000000000000];
+pub const ZIG_EXP_R: f64 = 7.697117470131050077;
+pub static ZIG_EXP_X: [f64; 257] =
+ [8.697117470131052741, 7.697117470131050077, 6.941033629377212577, 6.478378493832569696,
+ 6.144164665772472667, 5.882144315795399869, 5.666410167454033697, 5.482890627526062488,
+ 5.323090505754398016, 5.181487281301500047, 5.054288489981304089, 4.938777085901250530,
+ 4.832939741025112035, 4.735242996601741083, 4.644491885420085175, 4.559737061707351380,
+ 4.480211746528421912, 4.405287693473573185, 4.334443680317273007, 4.267242480277365857,
+ 4.203313713735184365, 4.142340865664051464, 4.084051310408297830, 4.028208544647936762,
+ 3.974606066673788796, 3.923062500135489739, 3.873417670399509127, 3.825529418522336744,
+ 3.779270992411667862, 3.734528894039797375, 3.691201090237418825, 3.649195515760853770,
+ 3.608428813128909507, 3.568825265648337020, 3.530315889129343354, 3.492837654774059608,
+ 3.456332821132760191, 3.420748357251119920, 3.386035442460300970, 3.352149030900109405,
+ 3.319047470970748037, 3.286692171599068679, 3.255047308570449882, 3.224079565286264160,
+ 3.193757903212240290, 3.164053358025972873, 3.134938858084440394, 3.106389062339824481,
+ 3.078380215254090224, 3.050890016615455114, 3.023897504455676621, 2.997382949516130601,
+ 2.971327759921089662, 2.945714394895045718, 2.920526286512740821, 2.895747768600141825,
+ 2.871364012015536371, 2.847360965635188812, 2.823725302450035279, 2.800444370250737780,
+ 2.777506146439756574, 2.754899196562344610, 2.732612636194700073, 2.710636095867928752,
+ 2.688959688741803689, 2.667573980773266573, 2.646469963151809157, 2.625639026797788489,
+ 2.605072938740835564, 2.584763820214140750, 2.564704126316905253, 2.544886627111869970,
+ 2.525304390037828028, 2.505950763528594027, 2.486819361740209455, 2.467904050297364815,
+ 2.449198932978249754, 2.430698339264419694, 2.412396812688870629, 2.394289099921457886,
+ 2.376370140536140596, 2.358635057409337321, 2.341079147703034380, 2.323697874390196372,
+ 2.306486858283579799, 2.289441870532269441, 2.272558825553154804, 2.255833774367219213,
+ 2.239262898312909034, 2.222842503111036816, 2.206569013257663858, 2.190438966723220027,
+ 2.174449009937774679, 2.158595893043885994, 2.142876465399842001, 2.127287671317368289,
+ 2.111826546019042183, 2.096490211801715020, 2.081275874393225145, 2.066180819490575526,
+ 2.051202409468584786, 2.036338080248769611, 2.021585338318926173, 2.006941757894518563,
+ 1.992404978213576650, 1.977972700957360441, 1.963642687789548313, 1.949412758007184943,
+ 1.935280786297051359, 1.921244700591528076, 1.907302480018387536, 1.893452152939308242,
+ 1.879691795072211180, 1.866019527692827973, 1.852433515911175554, 1.838931967018879954,
+ 1.825513128903519799, 1.812175288526390649, 1.798916770460290859, 1.785735935484126014,
+ 1.772631179231305643, 1.759600930889074766, 1.746643651946074405, 1.733757834985571566,
+ 1.720942002521935299, 1.708194705878057773, 1.695514524101537912, 1.682900062917553896,
+ 1.670349953716452118, 1.657862852574172763, 1.645437439303723659, 1.633072416535991334,
+ 1.620766508828257901, 1.608518461798858379, 1.596327041286483395, 1.584191032532688892,
+ 1.572109239386229707, 1.560080483527888084, 1.548103603714513499, 1.536177455041032092,
+ 1.524300908219226258, 1.512472848872117082, 1.500692176842816750, 1.488957805516746058,
+ 1.477268661156133867, 1.465623682245745352, 1.454021818848793446, 1.442462031972012504,
+ 1.430943292938879674, 1.419464582769983219, 1.408024891569535697, 1.396623217917042137,
+ 1.385258568263121992, 1.373929956328490576, 1.362636402505086775, 1.351376933258335189,
+ 1.340150580529504643, 1.328956381137116560, 1.317793376176324749, 1.306660610415174117,
+ 1.295557131686601027, 1.284481990275012642, 1.273434238296241139, 1.262412929069615330,
+ 1.251417116480852521, 1.240445854334406572, 1.229498195693849105, 1.218573192208790124,
+ 1.207669893426761121, 1.196787346088403092, 1.185924593404202199, 1.175080674310911677,
+ 1.164254622705678921, 1.153445466655774743, 1.142652227581672841, 1.131873919411078511,
+ 1.121109547701330200, 1.110358108727411031, 1.099618588532597308, 1.088889961938546813,
+ 1.078171191511372307, 1.067461226479967662, 1.056759001602551429, 1.046063435977044209,
+ 1.035373431790528542, 1.024687873002617211, 1.014005623957096480, 1.003325527915696735,
+ 0.992646405507275897, 0.981967053085062602, 0.971286240983903260, 0.960602711668666509,
+ 0.949915177764075969, 0.939222319955262286, 0.928522784747210395, 0.917815182070044311,
+ 0.907098082715690257, 0.896370015589889935, 0.885629464761751528, 0.874874866291025066,
+ 0.864104604811004484, 0.853317009842373353, 0.842510351810368485, 0.831682837734273206,
+ 0.820832606554411814, 0.809957724057418282, 0.799056177355487174, 0.788125868869492430,
+ 0.777164609759129710, 0.766170112735434672, 0.755139984181982249, 0.744071715500508102,
+ 0.732962673584365398, 0.721810090308756203, 0.710611050909655040, 0.699362481103231959,
+ 0.688061132773747808, 0.676703568029522584, 0.665286141392677943, 0.653804979847664947,
+ 0.642255960424536365, 0.630634684933490286, 0.618936451394876075, 0.607156221620300030,
+ 0.595288584291502887, 0.583327712748769489, 0.571267316532588332, 0.559100585511540626,
+ 0.546820125163310577, 0.534417881237165604, 0.521885051592135052, 0.509211982443654398,
+ 0.496388045518671162, 0.483401491653461857, 0.470239275082169006, 0.456886840931420235,
+ 0.443327866073552401, 0.429543940225410703, 0.415514169600356364, 0.401214678896277765,
+ 0.386617977941119573, 0.371692145329917234, 0.356399760258393816, 0.340696481064849122,
+ 0.324529117016909452, 0.307832954674932158, 0.290527955491230394, 0.272513185478464703,
+ 0.253658363385912022, 0.233790483059674731, 0.212671510630966620, 0.189958689622431842,
+ 0.165127622564187282, 0.137304980940012589, 0.104838507565818778, 0.063852163815001570,
+ 0.000000000000000000];
+pub static ZIG_EXP_F: [f64; 257] =
+ [0.000167066692307963, 0.000454134353841497, 0.000967269282327174, 0.001536299780301573,
+ 0.002145967743718907, 0.002788798793574076, 0.003460264777836904, 0.004157295120833797,
+ 0.004877655983542396, 0.005619642207205489, 0.006381905937319183, 0.007163353183634991,
+ 0.007963077438017043, 0.008780314985808977, 0.009614413642502212, 0.010464810181029981,
+ 0.011331013597834600, 0.012212592426255378, 0.013109164931254991, 0.014020391403181943,
+ 0.014945968011691148, 0.015885621839973156, 0.016839106826039941, 0.017806200410911355,
+ 0.018786700744696024, 0.019780424338009740, 0.020787204072578114, 0.021806887504283581,
+ 0.022839335406385240, 0.023884420511558174, 0.024942026419731787, 0.026012046645134221,
+ 0.027094383780955803, 0.028188948763978646, 0.029295660224637411, 0.030414443910466622,
+ 0.031545232172893622, 0.032687963508959555, 0.033842582150874358, 0.035009037697397431,
+ 0.036187284781931443, 0.037377282772959382, 0.038578995503074871, 0.039792391023374139,
+ 0.041017441380414840, 0.042254122413316254, 0.043502413568888197, 0.044762297732943289,
+ 0.046033761076175184, 0.047316792913181561, 0.048611385573379504, 0.049917534282706379,
+ 0.051235237055126281, 0.052564494593071685, 0.053905310196046080, 0.055257689676697030,
+ 0.056621641283742870, 0.057997175631200659, 0.059384305633420280, 0.060783046445479660,
+ 0.062193415408541036, 0.063615431999807376, 0.065049117786753805, 0.066494496385339816,
+ 0.067951593421936643, 0.069420436498728783, 0.070901055162371843, 0.072393480875708752,
+ 0.073897746992364746, 0.075413888734058410, 0.076941943170480517, 0.078481949201606435,
+ 0.080033947542319905, 0.081597980709237419, 0.083174093009632397, 0.084762330532368146,
+ 0.086362741140756927, 0.087975374467270231, 0.089600281910032886, 0.091237516631040197,
+ 0.092887133556043569, 0.094549189376055873, 0.096223742550432825, 0.097910853311492213,
+ 0.099610583670637132, 0.101322997425953631, 0.103048160171257702, 0.104786139306570145,
+ 0.106537004050001632, 0.108300825451033755, 0.110077676405185357, 0.111867631670056283,
+ 0.113670767882744286, 0.115487163578633506, 0.117316899211555525, 0.119160057175327641,
+ 0.121016721826674792, 0.122886979509545108, 0.124770918580830933, 0.126668629437510671,
+ 0.128580204545228199, 0.130505738468330773, 0.132445327901387494, 0.134399071702213602,
+ 0.136367070926428829, 0.138349428863580176, 0.140346251074862399, 0.142357645432472146,
+ 0.144383722160634720, 0.146424593878344889, 0.148480375643866735, 0.150551185001039839,
+ 0.152637142027442801, 0.154738369384468027, 0.156854992369365148, 0.158987138969314129,
+ 0.161134939917591952, 0.163298528751901734, 0.165478041874935922, 0.167673618617250081,
+ 0.169885401302527550, 0.172113535315319977, 0.174358169171353411, 0.176619454590494829,
+ 0.178897546572478278, 0.181192603475496261, 0.183504787097767436, 0.185834262762197083,
+ 0.188181199404254262, 0.190545769663195363, 0.192928149976771296, 0.195328520679563189,
+ 0.197747066105098818, 0.200183974691911210, 0.202639439093708962, 0.205113656293837654,
+ 0.207606827724221982, 0.210119159388988230, 0.212650861992978224, 0.215202151075378628,
+ 0.217773247148700472, 0.220364375843359439, 0.222975768058120111, 0.225607660116683956,
+ 0.228260293930716618, 0.230933917169627356, 0.233628783437433291, 0.236345152457059560,
+ 0.239083290262449094, 0.241843469398877131, 0.244625969131892024, 0.247431075665327543,
+ 0.250259082368862240, 0.253110290015629402, 0.255985007030415324, 0.258883549749016173,
+ 0.261806242689362922, 0.264753418835062149, 0.267725419932044739, 0.270722596799059967,
+ 0.273745309652802915, 0.276793928448517301, 0.279868833236972869, 0.282970414538780746,
+ 0.286099073737076826, 0.289255223489677693, 0.292439288161892630, 0.295651704281261252,
+ 0.298892921015581847, 0.302163400675693528, 0.305463619244590256, 0.308794066934560185,
+ 0.312155248774179606, 0.315547685227128949, 0.318971912844957239, 0.322428484956089223,
+ 0.325917972393556354, 0.329440964264136438, 0.332998068761809096, 0.336589914028677717,
+ 0.340217149066780189, 0.343880444704502575, 0.347580494621637148, 0.351318016437483449,
+ 0.355093752866787626, 0.358908472948750001, 0.362762973354817997, 0.366658079781514379,
+ 0.370594648435146223, 0.374573567615902381, 0.378595759409581067, 0.382662181496010056,
+ 0.386773829084137932, 0.390931736984797384, 0.395136981833290435, 0.399390684475231350,
+ 0.403694012530530555, 0.408048183152032673, 0.412454465997161457, 0.416914186433003209,
+ 0.421428728997616908, 0.425999541143034677, 0.430628137288459167, 0.435316103215636907,
+ 0.440065100842354173, 0.444876873414548846, 0.449753251162755330, 0.454696157474615836,
+ 0.459707615642138023, 0.464789756250426511, 0.469944825283960310, 0.475175193037377708,
+ 0.480483363930454543, 0.485871987341885248, 0.491343869594032867, 0.496901987241549881,
+ 0.502549501841348056, 0.508289776410643213, 0.514126393814748894, 0.520063177368233931,
+ 0.526104213983620062, 0.532253880263043655, 0.538516872002862246, 0.544898237672440056,
+ 0.551403416540641733, 0.558038282262587892, 0.564809192912400615, 0.571723048664826150,
+ 0.578787358602845359, 0.586010318477268366, 0.593400901691733762, 0.600968966365232560,
+ 0.608725382079622346, 0.616682180915207878, 0.624852738703666200, 0.633251994214366398,
+ 0.641896716427266423, 0.650805833414571433, 0.660000841079000145, 0.669506316731925177,
+ 0.679350572264765806, 0.689566496117078431, 0.700192655082788606, 0.711274760805076456,
+ 0.722867659593572465, 0.735038092431424039, 0.747868621985195658, 0.761463388849896838,
+ 0.775956852040116218, 0.791527636972496285, 0.808421651523009044, 0.826993296643051101,
+ 0.847785500623990496, 0.871704332381204705, 0.900469929925747703, 0.938143680862176477,
+ 1.000000000000000000];
diff --git a/rand/src/jitter.rs b/rand/src/jitter.rs
new file mode 100644
index 0000000..3693481
--- /dev/null
+++ b/rand/src/jitter.rs
@@ -0,0 +1,754 @@
+// Copyright 2017 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.
+//
+// Based on jitterentropy-library, http://www.chronox.de/jent.html.
+// Copyright Stephan Mueller <smueller@chronox.de>, 2014 - 2017.
+//
+// With permission from Stephan Mueller to relicense the Rust translation under
+// the MIT license.
+
+//! Non-physical true random number generator based on timing jitter.
+
+use Rng;
+
+use core::{fmt, mem, ptr};
+#[cfg(feature="std")]
+use std::sync::atomic::{AtomicUsize, ATOMIC_USIZE_INIT, Ordering};
+
+const MEMORY_BLOCKS: usize = 64;
+const MEMORY_BLOCKSIZE: usize = 32;
+const MEMORY_SIZE: usize = MEMORY_BLOCKS * MEMORY_BLOCKSIZE;
+
+/// A true random number generator based on jitter in the CPU execution time,
+/// and jitter in memory access time.
+///
+/// This is a true random number generator, as opposed to pseudo-random
+/// generators. Random numbers generated by `JitterRng` can be seen as fresh
+/// entropy. A consequence is that is orders of magnitude slower than `OsRng`
+/// and PRNGs (about 10^3 .. 10^6 slower).
+///
+/// There are very few situations where using this RNG is appropriate. Only very
+/// few applications require true entropy. A normal PRNG can be statistically
+/// indistinguishable, and a cryptographic PRNG should also be as impossible to
+/// predict.
+///
+/// Use of `JitterRng` is recommended for initializing cryptographic PRNGs when
+/// `OsRng` is not available.
+///
+/// This implementation is based on
+/// [Jitterentropy](http://www.chronox.de/jent.html) version 2.1.0.
+//
+// Note: the C implementation relies on being compiled without optimizations.
+// This implementation goes through lengths to make the compiler not optimise
+// out what is technically dead code, but that does influence timing jitter.
+pub struct JitterRng {
+ data: u64, // Actual random number
+ // Number of rounds to run the entropy collector per 64 bits
+ rounds: u32,
+ // Timer and previous time stamp, used by `measure_jitter`
+ timer: fn() -> u64,
+ prev_time: u64,
+ // Deltas used for the stuck test
+ last_delta: i64,
+ last_delta2: i64,
+ // Memory for the Memory Access noise source
+ mem_prev_index: usize,
+ mem: [u8; MEMORY_SIZE],
+ // Make `next_u32` not waste 32 bits
+ data_remaining: Option<u32>,
+}
+
+// Custom Debug implementation that does not expose the internal state
+impl fmt::Debug for JitterRng {
+ fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+ write!(f, "JitterRng {{}}")
+ }
+}
+
+/// An error that can occur when `test_timer` fails.
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub enum TimerError {
+ /// No timer available.
+ NoTimer,
+ /// Timer too coarse to use as an entropy source.
+ CoarseTimer,
+ /// Timer is not monotonically increasing.
+ NotMonotonic,
+ /// Variations of deltas of time too small.
+ TinyVariantions,
+ /// Too many stuck results (indicating no added entropy).
+ TooManyStuck,
+ #[doc(hidden)]
+ __Nonexhaustive,
+}
+
+impl TimerError {
+ fn description(&self) -> &'static str {
+ match *self {
+ TimerError::NoTimer => "no timer available",
+ TimerError::CoarseTimer => "coarse timer",
+ TimerError::NotMonotonic => "timer not monotonic",
+ TimerError::TinyVariantions => "time delta variations too small",
+ TimerError::TooManyStuck => "too many stuck results",
+ TimerError::__Nonexhaustive => unreachable!(),
+ }
+ }
+}
+
+impl fmt::Display for TimerError {
+ fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+ write!(f, "{}", self.description())
+ }
+}
+
+#[cfg(feature="std")]
+impl ::std::error::Error for TimerError {
+ fn description(&self) -> &str {
+ self.description()
+ }
+}
+
+// Initialise to zero; must be positive
+#[cfg(feature="std")]
+static JITTER_ROUNDS: AtomicUsize = ATOMIC_USIZE_INIT;
+
+impl JitterRng {
+ /// Create a new `JitterRng`.
+ /// Makes use of `std::time` for a timer.
+ ///
+ /// During initialization CPU execution timing jitter is measured a few
+ /// hundred times. If this does not pass basic quality tests, an error is
+ /// returned. The test result is cached to make subsequent calls faster.
+ #[cfg(feature="std")]
+ pub fn new() -> Result<JitterRng, TimerError> {
+ let mut ec = JitterRng::new_with_timer(platform::get_nstime);
+ let mut rounds = JITTER_ROUNDS.load(Ordering::Relaxed) as u32;
+ if rounds == 0 {
+ // No result yet: run test.
+ // This allows the timer test to run multiple times; we don't care.
+ rounds = ec.test_timer()?;
+ JITTER_ROUNDS.store(rounds as usize, Ordering::Relaxed);
+ }
+ ec.set_rounds(rounds);
+ Ok(ec)
+ }
+
+ /// Create a new `JitterRng`.
+ /// A custom timer can be supplied, making it possible to use `JitterRng` in
+ /// `no_std` environments.
+ ///
+ /// The timer must have nanosecond precision.
+ ///
+ /// This method is more low-level than `new()`. It is the responsibility of
+ /// the caller to run `test_timer` before using any numbers generated with
+ /// `JitterRng`, and optionally call `set_rounds()`.
+ pub fn new_with_timer(timer: fn() -> u64) -> JitterRng {
+ let mut ec = JitterRng {
+ data: 0,
+ rounds: 64,
+ timer: timer,
+ prev_time: 0,
+ last_delta: 0,
+ last_delta2: 0,
+ mem_prev_index: 0,
+ mem: [0; MEMORY_SIZE],
+ data_remaining: None,
+ };
+
+ // Fill `data`, `prev_time`, `last_delta` and `last_delta2` with
+ // non-zero values.
+ ec.prev_time = timer();
+ ec.gen_entropy();
+
+ // Do a single read from `self.mem` to make sure the Memory Access noise
+ // source is not optimised out.
+ // Note: this read is important, it effects optimisations for the entire
+ // module!
+ black_box(ec.mem[0]);
+
+ ec
+ }
+
+ /// Configures how many rounds are used to generate each 64-bit value.
+ /// This must be greater than zero, and has a big impact on performance
+ /// and output quality.
+ ///
+ /// `new_with_timer` conservatively uses 64 rounds, but often less rounds
+ /// can be used. The `test_timer()` function returns the minimum number of
+ /// rounds required for full strength (platform dependent), so one may use
+ /// `rng.set_rounds(rng.test_timer()?);` or cache the value.
+ pub fn set_rounds(&mut self, rounds: u32) {
+ assert!(rounds > 0);
+ self.rounds = rounds;
+ }
+
+ // Calculate a random loop count used for the next round of an entropy
+ // collection, based on bits from a fresh value from the timer.
+ //
+ // The timer is folded to produce a number that contains at most `n_bits`
+ // bits.
+ //
+ // Note: A constant should be added to the resulting random loop count to
+ // prevent loops that run 0 times.
+ #[inline(never)]
+ fn random_loop_cnt(&mut self, n_bits: u32) -> u32 {
+ let mut rounds = 0;
+
+ let mut time = (self.timer)();
+ // Mix with the current state of the random number balance the random
+ // loop counter a bit more.
+ time ^= self.data;
+
+ // We fold the time value as much as possible to ensure that as many
+ // bits of the time stamp are included as possible.
+ let folds = (64 + n_bits - 1) / n_bits;
+ let mask = (1 << n_bits) - 1;
+ for _ in 0..folds {
+ rounds ^= time & mask;
+ time = time >> n_bits;
+ }
+
+ rounds as u32
+ }
+
+ // CPU jitter noise source
+ // Noise source based on the CPU execution time jitter
+ //
+ // This function injects the individual bits of the time value into the
+ // entropy pool using an LFSR.
+ //
+ // The code is deliberately inefficient with respect to the bit shifting.
+ // This function not only acts as folding operation, but this function's
+ // execution is used to measure the CPU execution time jitter. Any change to
+ // the loop in this function implies that careful retesting must be done.
+ #[inline(never)]
+ fn lfsr_time(&mut self, time: u64, var_rounds: bool) {
+ fn lfsr(mut data: u64, time: u64) -> u64{
+ for i in 1..65 {
+ let mut tmp = time << (64 - i);
+ tmp = tmp >> (64 - 1);
+
+ // Fibonacci LSFR with polynomial of
+ // x^64 + x^61 + x^56 + x^31 + x^28 + x^23 + 1 which is
+ // primitive according to
+ // http://poincare.matf.bg.ac.rs/~ezivkovm/publications/primpol1.pdf
+ // (the shift values are the polynomial values minus one
+ // due to counting bits from 0 to 63). As the current
+ // position is always the LSB, the polynomial only needs
+ // to shift data in from the left without wrap.
+ data ^= tmp;
+ data ^= (data >> 63) & 1;
+ data ^= (data >> 60) & 1;
+ data ^= (data >> 55) & 1;
+ data ^= (data >> 30) & 1;
+ data ^= (data >> 27) & 1;
+ data ^= (data >> 22) & 1;
+ data = data.rotate_left(1);
+ }
+ data
+ }
+
+ // Note: in the reference implementation only the last round effects
+ // `self.data`, all the other results are ignored. To make sure the
+ // other rounds are not optimised out, we first run all but the last
+ // round on a throw-away value instead of the real `self.data`.
+ let mut lfsr_loop_cnt = 0;
+ if var_rounds { lfsr_loop_cnt = self.random_loop_cnt(4) };
+
+ let mut throw_away: u64 = 0;
+ for _ in 0..lfsr_loop_cnt {
+ throw_away = lfsr(throw_away, time);
+ }
+ black_box(throw_away);
+
+ self.data = lfsr(self.data, time);
+ }
+
+ // Memory Access noise source
+ // This is a noise source based on variations in memory access times
+ //
+ // This function performs memory accesses which will add to the timing
+ // variations due to an unknown amount of CPU wait states that need to be
+ // added when accessing memory. The memory size should be larger than the L1
+ // caches as outlined in the documentation and the associated testing.
+ //
+ // The L1 cache has a very high bandwidth, albeit its access rate is usually
+ // slower than accessing CPU registers. Therefore, L1 accesses only add
+ // minimal variations as the CPU has hardly to wait. Starting with L2,
+ // significant variations are added because L2 typically does not belong to
+ // the CPU any more and therefore a wider range of CPU wait states is
+ // necessary for accesses. L3 and real memory accesses have even a wider
+ // range of wait states. However, to reliably access either L3 or memory,
+ // the `self.mem` memory must be quite large which is usually not desirable.
+ #[inline(never)]
+ fn memaccess(&mut self, var_rounds: bool) {
+ let mut acc_loop_cnt = 128;
+ if var_rounds { acc_loop_cnt += self.random_loop_cnt(4) };
+
+ let mut index = self.mem_prev_index;
+ for _ in 0..acc_loop_cnt {
+ // Addition of memblocksize - 1 to index with wrap around logic to
+ // ensure that every memory location is hit evenly.
+ // The modulus also allows the compiler to remove the indexing
+ // bounds check.
+ index = (index + MEMORY_BLOCKSIZE - 1) % MEMORY_SIZE;
+
+ // memory access: just add 1 to one byte
+ // memory access implies read from and write to memory location
+ let tmp = self.mem[index];
+ self.mem[index] = tmp.wrapping_add(1);
+ }
+ self.mem_prev_index = index;
+ }
+
+
+ // Stuck test by checking the:
+ // - 1st derivation of the jitter measurement (time delta)
+ // - 2nd derivation of the jitter measurement (delta of time deltas)
+ // - 3rd derivation of the jitter measurement (delta of delta of time
+ // deltas)
+ //
+ // All values must always be non-zero.
+ // This test is a heuristic to see whether the last measurement holds
+ // entropy.
+ fn stuck(&mut self, current_delta: i64) -> bool {
+ let delta2 = self.last_delta - current_delta;
+ let delta3 = delta2 - self.last_delta2;
+
+ self.last_delta = current_delta;
+ self.last_delta2 = delta2;
+
+ current_delta == 0 || delta2 == 0 || delta3 == 0
+ }
+
+ // This is the heart of the entropy generation: calculate time deltas and
+ // use the CPU jitter in the time deltas. The jitter is injected into the
+ // entropy pool.
+ //
+ // Ensure that `self.prev_time` is primed before using the output of this
+ // function. This can be done by calling this function and not using its
+ // result.
+ fn measure_jitter(&mut self) -> Option<()> {
+ // Invoke one noise source before time measurement to add variations
+ self.memaccess(true);
+
+ // Get time stamp and calculate time delta to previous
+ // invocation to measure the timing variations
+ let time = (self.timer)();
+ // Note: wrapping_sub combined with a cast to `i64` generates a correct
+ // delta, even in the unlikely case this is a timer that is not strictly
+ // monotonic.
+ let current_delta = time.wrapping_sub(self.prev_time) as i64;
+ self.prev_time = time;
+
+ // Call the next noise source which also injects the data
+ self.lfsr_time(current_delta as u64, true);
+
+ // Check whether we have a stuck measurement (i.e. does the last
+ // measurement holds entropy?).
+ if self.stuck(current_delta) { return None };
+
+ // Rotate the data buffer by a prime number (any odd number would
+ // do) to ensure that every bit position of the input time stamp
+ // has an even chance of being merged with a bit position in the
+ // entropy pool. We do not use one here as the adjacent bits in
+ // successive time deltas may have some form of dependency. The
+ // chosen value of 7 implies that the low 7 bits of the next
+ // time delta value is concatenated with the current time delta.
+ self.data = self.data.rotate_left(7);
+
+ Some(())
+ }
+
+ // Shuffle the pool a bit by mixing some value with a bijective function
+ // (XOR) into the pool.
+ //
+ // The function generates a mixer value that depends on the bits set and
+ // the location of the set bits in the random number generated by the
+ // entropy source. Therefore, based on the generated random number, this
+ // mixer value can have 2^64 different values. That mixer value is
+ // initialized with the first two SHA-1 constants. After obtaining the
+ // mixer value, it is XORed into the random number.
+ //
+ // The mixer value is not assumed to contain any entropy. But due to the
+ // XOR operation, it can also not destroy any entropy present in the
+ // entropy pool.
+ #[inline(never)]
+ fn stir_pool(&mut self) {
+ // This constant is derived from the first two 32 bit initialization
+ // vectors of SHA-1 as defined in FIPS 180-4 section 5.3.1
+ // The order does not really matter as we do not rely on the specific
+ // numbers. We just pick the SHA-1 constants as they have a good mix of
+ // bit set and unset.
+ const CONSTANT: u64 = 0x67452301efcdab89;
+
+ // The start value of the mixer variable is derived from the third
+ // and fourth 32 bit initialization vector of SHA-1 as defined in
+ // FIPS 180-4 section 5.3.1
+ let mut mixer = 0x98badcfe10325476;
+
+ // This is a constant time function to prevent leaking timing
+ // information about the random number.
+ // The normal code is:
+ // ```
+ // for i in 0..64 {
+ // if ((self.data >> i) & 1) == 1 { mixer ^= CONSTANT; }
+ // }
+ // ```
+ // This is a bit fragile, as LLVM really wants to use branches here, and
+ // we rely on it to not recognise the opportunity.
+ for i in 0..64 {
+ let apply = (self.data >> i) & 1;
+ let mask = !apply.wrapping_sub(1);
+ mixer ^= CONSTANT & mask;
+ mixer = mixer.rotate_left(1);
+ }
+
+ self.data ^= mixer;
+ }
+
+ fn gen_entropy(&mut self) -> u64 {
+ // Prime `self.prev_time`, and run the noice sources to make sure the
+ // first loop round collects the expected entropy.
+ let _ = self.measure_jitter();
+
+ for _ in 0..self.rounds {
+ // If a stuck measurement is received, repeat measurement
+ // Note: we do not guard against an infinite loop, that would mean
+ // the timer suddenly became broken.
+ while self.measure_jitter().is_none() {}
+ }
+
+ self.stir_pool();
+ self.data
+ }
+
+ /// Basic quality tests on the timer, by measuring CPU timing jitter a few
+ /// hundred times.
+ ///
+ /// If succesful, this will return the estimated number of rounds necessary
+ /// to collect 64 bits of entropy. Otherwise a `TimerError` with the cause
+ /// of the failure will be returned.
+ pub fn test_timer(&mut self) -> Result<u32, TimerError> {
+ // We could add a check for system capabilities such as `clock_getres`
+ // or check for `CONFIG_X86_TSC`, but it does not make much sense as the
+ // following sanity checks verify that we have a high-resolution timer.
+
+ #[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))]
+ return Err(TimerError::NoTimer);
+
+ let mut delta_sum = 0;
+ let mut old_delta = 0;
+
+ let mut time_backwards = 0;
+ let mut count_mod = 0;
+ let mut count_stuck = 0;
+
+ // TESTLOOPCOUNT needs some loops to identify edge systems.
+ // 100 is definitely too little.
+ const TESTLOOPCOUNT: u64 = 300;
+ const CLEARCACHE: u64 = 100;
+
+ for i in 0..(CLEARCACHE + TESTLOOPCOUNT) {
+ // Measure time delta of core entropy collection logic
+ let time = (self.timer)();
+ self.memaccess(true);
+ self.lfsr_time(time, true);
+ let time2 = (self.timer)();
+
+ // Test whether timer works
+ if time == 0 || time2 == 0 {
+ return Err(TimerError::NoTimer);
+ }
+ let delta = time2.wrapping_sub(time) as i64;
+
+ // Test whether timer is fine grained enough to provide delta even
+ // when called shortly after each other -- this implies that we also
+ // have a high resolution timer
+ if delta == 0 {
+ return Err(TimerError::CoarseTimer);
+ }
+
+ // Up to here we did not modify any variable that will be
+ // evaluated later, but we already performed some work. Thus we
+ // already have had an impact on the caches, branch prediction,
+ // etc. with the goal to clear it to get the worst case
+ // measurements.
+ if i < CLEARCACHE { continue; }
+
+ if self.stuck(delta) { count_stuck += 1; }
+
+ // Test whether we have an increasing timer.
+ if !(time2 > time) { time_backwards += 1; }
+
+ // Count the number of times the counter increases in steps of 100ns
+ // or greater.
+ if (delta % 100) == 0 { count_mod += 1; }
+
+ // Ensure that we have a varying delta timer which is necessary for
+ // the calculation of entropy -- perform this check only after the
+ // first loop is executed as we need to prime the old_delta value
+ delta_sum += (delta - old_delta).abs() as u64;
+ old_delta = delta;
+ }
+
+ // We allow the time to run backwards for up to three times.
+ // This can happen if the clock is being adjusted by NTP operations.
+ // If such an operation just happens to interfere with our test, it
+ // should not fail. The value of 3 should cover the NTP case being
+ // performed during our test run.
+ if time_backwards > 3 {
+ return Err(TimerError::NotMonotonic);
+ }
+
+ // Test that the available amount of entropy per round does not get to
+ // low. We expect 1 bit of entropy per round as a reasonable minimum
+ // (although less is possible, it means the collector loop has to run
+ // much more often).
+ // `assert!(delta_average >= log2(1))`
+ // `assert!(delta_sum / TESTLOOPCOUNT >= 1)`
+ // `assert!(delta_sum >= TESTLOOPCOUNT)`
+ if delta_sum < TESTLOOPCOUNT {
+ return Err(TimerError::TinyVariantions);
+ }
+
+ // Ensure that we have variations in the time stamp below 100 for at
+ // least 10% of all checks -- on some platforms, the counter increments
+ // in multiples of 100, but not always
+ if count_mod > (TESTLOOPCOUNT * 9 / 10) {
+ return Err(TimerError::CoarseTimer);
+ }
+
+ // If we have more than 90% stuck results, then this Jitter RNG is
+ // likely to not work well.
+ if count_stuck > (TESTLOOPCOUNT * 9 / 10) {
+ return Err(TimerError::TooManyStuck);
+ }
+
+ // Estimate the number of `measure_jitter` rounds necessary for 64 bits
+ // of entropy.
+ //
+ // We don't try very hard to come up with a good estimate of the
+ // available bits of entropy per round here for two reasons:
+ // 1. Simple estimates of the available bits (like Shannon entropy) are
+ // too optimistic.
+ // 2) Unless we want to waste a lot of time during intialization, there
+ // only a small number of samples are available.
+ //
+ // Therefore we use a very simple and conservative estimate:
+ // `let bits_of_entropy = log2(delta_average) / 2`.
+ //
+ // The number of rounds `measure_jitter` should run to collect 64 bits
+ // of entropy is `64 / bits_of_entropy`.
+ //
+ // To have smaller rounding errors, intermediate values are multiplied
+ // by `FACTOR`. To compensate for `log2` and division rounding down,
+ // add 1.
+ let delta_average = delta_sum / TESTLOOPCOUNT;
+ // println!("delta_average: {}", delta_average);
+
+ const FACTOR: u32 = 3;
+ fn log2(x: u64) -> u32 { 64 - x.leading_zeros() }
+
+ // pow(δ, FACTOR) must be representable; if you have overflow reduce FACTOR
+ Ok(64 * 2 * FACTOR / (log2(delta_average.pow(FACTOR)) + 1))
+ }
+
+ /// Statistical test: return the timer delta of one normal run of the
+ /// `JitterEntropy` entropy collector.
+ ///
+ /// Setting `var_rounds` to `true` will execute the memory access and the
+ /// CPU jitter noice sources a variable amount of times (just like a real
+ /// `JitterEntropy` round).
+ ///
+ /// Setting `var_rounds` to `false` will execute the noice sources the
+ /// minimal number of times. This can be used to measure the minimum amount
+ /// of entropy one round of entropy collector can collect in the worst case.
+ ///
+ /// # Example
+ ///
+ /// Use `timer_stats` to run the [NIST SP 800-90B Entropy Estimation Suite]
+ /// (https://github.com/usnistgov/SP800-90B_EntropyAssessment).
+ ///
+ /// This is the recommended way to test the quality of `JitterRng`. It
+ /// should be run before using the RNG on untested hardware, after changes
+ /// that could effect how the code is optimised, and after major compiler
+ /// compiler changes, like a new LLVM version.
+ ///
+ /// First generate two files `jitter_rng_var.bin` and `jitter_rng_var.min`.
+ ///
+ /// Execute `python noniid_main.py -v jitter_rng_var.bin 8`, and validate it
+ /// with `restart.py -v jitter_rng_var.bin 8 <min-entropy>`.
+ /// This number is the expected amount of entropy that is at least available
+ /// for each round of the entropy collector. This number should be greater
+ /// than the amount estimated with `64 / test_timer()`.
+ ///
+ /// Execute `python noniid_main.py -v -u 4 jitter_rng_var.bin 4`, and
+ /// validate it with `restart.py -v -u 4 jitter_rng_var.bin 4 <min-entropy>`.
+ /// This number is the expected amount of entropy that is available in the
+ /// last 4 bits of the timer delta after running noice sources. Note that
+ /// a value of 3.70 is the minimum estimated entropy for true randomness.
+ ///
+ /// Execute `python noniid_main.py -v -u 4 jitter_rng_var.bin 4`, and
+ /// validate it with `restart.py -v -u 4 jitter_rng_var.bin 4 <min-entropy>`.
+ /// This number is the expected amount of entropy that is available to the
+ /// entropy collecter if both noice sources only run their minimal number of
+ /// times. This measures the absolute worst-case, and gives a lower bound
+ /// for the available entropy.
+ ///
+ /// ```rust,no_run
+ /// use rand::JitterRng;
+ ///
+ /// # use std::error::Error;
+ /// # use std::fs::File;
+ /// # use std::io::Write;
+ /// #
+ /// # fn try_main() -> Result<(), Box<Error>> {
+ /// fn get_nstime() -> u64 {
+ /// use std::time::{SystemTime, UNIX_EPOCH};
+ ///
+ /// let dur = SystemTime::now().duration_since(UNIX_EPOCH).unwrap();
+ /// // The correct way to calculate the current time is
+ /// // `dur.as_secs() * 1_000_000_000 + dur.subsec_nanos() as u64`
+ /// // But this is faster, and the difference in terms of entropy is
+ /// // negligible (log2(10^9) == 29.9).
+ /// dur.as_secs() << 30 | dur.subsec_nanos() as u64
+ /// }
+ ///
+ /// // Do not initialize with `JitterRng::new`, but with `new_with_timer`.
+ /// // 'new' always runst `test_timer`, and can therefore fail to
+ /// // initialize. We want to be able to get the statistics even when the
+ /// // timer test fails.
+ /// let mut rng = JitterRng::new_with_timer(get_nstime);
+ ///
+ /// // 1_000_000 results are required for the NIST SP 800-90B Entropy
+ /// // Estimation Suite
+ /// // FIXME: this number is smaller here, otherwise the Doc-test is too slow
+ /// const ROUNDS: usize = 10_000;
+ /// let mut deltas_variable: Vec<u8> = Vec::with_capacity(ROUNDS);
+ /// let mut deltas_minimal: Vec<u8> = Vec::with_capacity(ROUNDS);
+ ///
+ /// for _ in 0..ROUNDS {
+ /// deltas_variable.push(rng.timer_stats(true) as u8);
+ /// deltas_minimal.push(rng.timer_stats(false) as u8);
+ /// }
+ ///
+ /// // Write out after the statistics collection loop, to not disturb the
+ /// // test results.
+ /// File::create("jitter_rng_var.bin")?.write(&deltas_variable)?;
+ /// File::create("jitter_rng_min.bin")?.write(&deltas_minimal)?;
+ /// #
+ /// # Ok(())
+ /// # }
+ /// #
+ /// # fn main() {
+ /// # try_main().unwrap();
+ /// # }
+ /// ```
+ #[cfg(feature="std")]
+ pub fn timer_stats(&mut self, var_rounds: bool) -> i64 {
+ let time = platform::get_nstime();
+ self.memaccess(var_rounds);
+ self.lfsr_time(time, var_rounds);
+ let time2 = platform::get_nstime();
+ time2.wrapping_sub(time) as i64
+ }
+}
+
+#[cfg(feature="std")]
+mod platform {
+ #[cfg(not(any(target_os = "macos", target_os = "ios", target_os = "windows", all(target_arch = "wasm32", not(target_os = "emscripten")))))]
+ pub fn get_nstime() -> u64 {
+ use std::time::{SystemTime, UNIX_EPOCH};
+
+ let dur = SystemTime::now().duration_since(UNIX_EPOCH).unwrap();
+ // The correct way to calculate the current time is
+ // `dur.as_secs() * 1_000_000_000 + dur.subsec_nanos() as u64`
+ // But this is faster, and the difference in terms of entropy is negligible
+ // (log2(10^9) == 29.9).
+ dur.as_secs() << 30 | dur.subsec_nanos() as u64
+ }
+
+ #[cfg(any(target_os = "macos", target_os = "ios"))]
+ pub fn get_nstime() -> u64 {
+ extern crate libc;
+ // On Mac OS and iOS std::time::SystemTime only has 1000ns resolution.
+ // We use `mach_absolute_time` instead. This provides a CPU dependent unit,
+ // to get real nanoseconds the result should by multiplied by numer/denom
+ // from `mach_timebase_info`.
+ // But we are not interested in the exact nanoseconds, just entropy. So we
+ // use the raw result.
+ unsafe { libc::mach_absolute_time() }
+ }
+
+ #[cfg(target_os = "windows")]
+ pub fn get_nstime() -> u64 {
+ extern crate winapi;
+ unsafe {
+ let mut t = super::mem::zeroed();
+ winapi::um::profileapi::QueryPerformanceCounter(&mut t);
+ *t.QuadPart() as u64
+ }
+ }
+
+ #[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))]
+ pub fn get_nstime() -> u64 {
+ unreachable!()
+ }
+}
+
+// A function that is opaque to the optimizer to assist in avoiding dead-code
+// elimination. Taken from `bencher`.
+fn black_box<T>(dummy: T) -> T {
+ unsafe {
+ let ret = ptr::read_volatile(&dummy);
+ mem::forget(dummy);
+ ret
+ }
+}
+
+impl Rng for JitterRng {
+ fn next_u32(&mut self) -> u32 {
+ // We want to use both parts of the generated entropy
+ if let Some(high) = self.data_remaining.take() {
+ high
+ } else {
+ let data = self.next_u64();
+ self.data_remaining = Some((data >> 32) as u32);
+ data as u32
+ }
+ }
+
+ fn next_u64(&mut self) -> u64 {
+ self.gen_entropy()
+ }
+
+ fn fill_bytes(&mut self, dest: &mut [u8]) {
+ let mut left = dest;
+ while left.len() >= 8 {
+ let (l, r) = {left}.split_at_mut(8);
+ left = r;
+ let chunk: [u8; 8] = unsafe {
+ mem::transmute(self.next_u64().to_le())
+ };
+ l.copy_from_slice(&chunk);
+ }
+ let n = left.len();
+ if n > 0 {
+ let chunk: [u8; 8] = unsafe {
+ mem::transmute(self.next_u64().to_le())
+ };
+ left.copy_from_slice(&chunk[..n]);
+ }
+ }
+}
+
+// There are no tests included because (1) this is an "external" RNG, so output
+// is not reproducible and (2) `test_timer` *will* fail on some platforms.
diff --git a/rand/src/lib.rs b/rand/src/lib.rs
new file mode 100644
index 0000000..7b22dd4
--- /dev/null
+++ b/rand/src/lib.rs
@@ -0,0 +1,1214 @@
+// Copyright 2013-2017 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.
+
+//! Utilities for random number generation
+//!
+//! The key functions are `random()` and `Rng::gen()`. These are polymorphic and
+//! so can be used to generate any type that implements `Rand`. Type inference
+//! means that often a simple call to `rand::random()` or `rng.gen()` will
+//! suffice, but sometimes an annotation is required, e.g.
+//! `rand::random::<f64>()`.
+//!
+//! See the `distributions` submodule for sampling random numbers from
+//! distributions like normal and exponential.
+//!
+//! # Usage
+//!
+//! This crate is [on crates.io](https://crates.io/crates/rand) and can be
+//! used by adding `rand` to the dependencies in your project's `Cargo.toml`.
+//!
+//! ```toml
+//! [dependencies]
+//! rand = "0.4"
+//! ```
+//!
+//! and this to your crate root:
+//!
+//! ```rust
+//! extern crate rand;
+//! ```
+//!
+//! # Thread-local RNG
+//!
+//! There is built-in support for a RNG associated with each thread stored
+//! in thread-local storage. This RNG can be accessed via `thread_rng`, or
+//! used implicitly via `random`. This RNG is normally randomly seeded
+//! from an operating-system source of randomness, e.g. `/dev/urandom` on
+//! Unix systems, and will automatically reseed itself from this source
+//! after generating 32 KiB of random data.
+//!
+//! # Cryptographic security
+//!
+//! An application that requires an entropy source for cryptographic purposes
+//! must use `OsRng`, which reads randomness from the source that the operating
+//! system provides (e.g. `/dev/urandom` on Unixes or `CryptGenRandom()` on
+//! Windows).
+//! The other random number generators provided by this module are not suitable
+//! for such purposes.
+//!
+//! *Note*: many Unix systems provide `/dev/random` as well as `/dev/urandom`.
+//! This module uses `/dev/urandom` for the following reasons:
+//!
+//! - On Linux, `/dev/random` may block if entropy pool is empty;
+//! `/dev/urandom` will not block. This does not mean that `/dev/random`
+//! provides better output than `/dev/urandom`; the kernel internally runs a
+//! cryptographically secure pseudorandom number generator (CSPRNG) based on
+//! entropy pool for random number generation, so the "quality" of
+//! `/dev/random` is not better than `/dev/urandom` in most cases. However,
+//! this means that `/dev/urandom` can yield somewhat predictable randomness
+//! if the entropy pool is very small, such as immediately after first
+//! booting. Linux 3.17 added the `getrandom(2)` system call which solves
+//! the issue: it blocks if entropy pool is not initialized yet, but it does
+//! not block once initialized. `OsRng` tries to use `getrandom(2)` if
+//! available, and use `/dev/urandom` fallback if not. If an application
+//! does not have `getrandom` and likely to be run soon after first booting,
+//! or on a system with very few entropy sources, one should consider using
+//! `/dev/random` via `ReadRng`.
+//! - On some systems (e.g. FreeBSD, OpenBSD and Mac OS X) there is no
+//! difference between the two sources. (Also note that, on some systems
+//! e.g. FreeBSD, both `/dev/random` and `/dev/urandom` may block once if
+//! the CSPRNG has not seeded yet.)
+//!
+//! # Examples
+//!
+//! ```rust
+//! use rand::Rng;
+//!
+//! let mut rng = rand::thread_rng();
+//! if rng.gen() { // random bool
+//! println!("i32: {}, u32: {}", rng.gen::<i32>(), rng.gen::<u32>())
+//! }
+//! ```
+//!
+//! ```rust
+//! let tuple = rand::random::<(f64, char)>();
+//! println!("{:?}", tuple)
+//! ```
+//!
+//! ## Monte Carlo estimation of π
+//!
+//! For this example, imagine we have a square with sides of length 2 and a unit
+//! circle, both centered at the origin. Since the area of a unit circle is π,
+//! we have:
+//!
+//! ```text
+//! (area of unit circle) / (area of square) = π / 4
+//! ```
+//!
+//! So if we sample many points randomly from the square, roughly π / 4 of them
+//! should be inside the circle.
+//!
+//! We can use the above fact to estimate the value of π: pick many points in
+//! the square at random, calculate the fraction that fall within the circle,
+//! and multiply this fraction by 4.
+//!
+//! ```
+//! use rand::distributions::{IndependentSample, Range};
+//!
+//! fn main() {
+//! let between = Range::new(-1f64, 1.);
+//! let mut rng = rand::thread_rng();
+//!
+//! let total = 1_000_000;
+//! let mut in_circle = 0;
+//!
+//! for _ in 0..total {
+//! let a = between.ind_sample(&mut rng);
+//! let b = between.ind_sample(&mut rng);
+//! if a*a + b*b <= 1. {
+//! in_circle += 1;
+//! }
+//! }
+//!
+//! // prints something close to 3.14159...
+//! println!("{}", 4. * (in_circle as f64) / (total as f64));
+//! }
+//! ```
+//!
+//! ## Monty Hall Problem
+//!
+//! This is a simulation of the [Monty Hall Problem][]:
+//!
+//! > Suppose you're on a game show, and you're given the choice of three doors:
+//! > Behind one door is a car; behind the others, goats. You pick a door, say
+//! > No. 1, and the host, who knows what's behind the doors, opens another
+//! > door, say No. 3, which has a goat. He then says to you, "Do you want to
+//! > pick door No. 2?" Is it to your advantage to switch your choice?
+//!
+//! The rather unintuitive answer is that you will have a 2/3 chance of winning
+//! if you switch and a 1/3 chance of winning if you don't, so it's better to
+//! switch.
+//!
+//! This program will simulate the game show and with large enough simulation
+//! steps it will indeed confirm that it is better to switch.
+//!
+//! [Monty Hall Problem]: http://en.wikipedia.org/wiki/Monty_Hall_problem
+//!
+//! ```
+//! use rand::Rng;
+//! use rand::distributions::{IndependentSample, Range};
+//!
+//! struct SimulationResult {
+//! win: bool,
+//! switch: bool,
+//! }
+//!
+//! // Run a single simulation of the Monty Hall problem.
+//! fn simulate<R: Rng>(random_door: &Range<u32>, rng: &mut R)
+//! -> SimulationResult {
+//! let car = random_door.ind_sample(rng);
+//!
+//! // This is our initial choice
+//! let mut choice = random_door.ind_sample(rng);
+//!
+//! // The game host opens a door
+//! let open = game_host_open(car, choice, rng);
+//!
+//! // Shall we switch?
+//! let switch = rng.gen();
+//! if switch {
+//! choice = switch_door(choice, open);
+//! }
+//!
+//! SimulationResult { win: choice == car, switch: switch }
+//! }
+//!
+//! // Returns the door the game host opens given our choice and knowledge of
+//! // where the car is. The game host will never open the door with the car.
+//! fn game_host_open<R: Rng>(car: u32, choice: u32, rng: &mut R) -> u32 {
+//! let choices = free_doors(&[car, choice]);
+//! rand::seq::sample_slice(rng, &choices, 1)[0]
+//! }
+//!
+//! // Returns the door we switch to, given our current choice and
+//! // the open door. There will only be one valid door.
+//! fn switch_door(choice: u32, open: u32) -> u32 {
+//! free_doors(&[choice, open])[0]
+//! }
+//!
+//! fn free_doors(blocked: &[u32]) -> Vec<u32> {
+//! (0..3).filter(|x| !blocked.contains(x)).collect()
+//! }
+//!
+//! fn main() {
+//! // The estimation will be more accurate with more simulations
+//! let num_simulations = 10000;
+//!
+//! let mut rng = rand::thread_rng();
+//! let random_door = Range::new(0, 3);
+//!
+//! let (mut switch_wins, mut switch_losses) = (0, 0);
+//! let (mut keep_wins, mut keep_losses) = (0, 0);
+//!
+//! println!("Running {} simulations...", num_simulations);
+//! for _ in 0..num_simulations {
+//! let result = simulate(&random_door, &mut rng);
+//!
+//! match (result.win, result.switch) {
+//! (true, true) => switch_wins += 1,
+//! (true, false) => keep_wins += 1,
+//! (false, true) => switch_losses += 1,
+//! (false, false) => keep_losses += 1,
+//! }
+//! }
+//!
+//! let total_switches = switch_wins + switch_losses;
+//! let total_keeps = keep_wins + keep_losses;
+//!
+//! println!("Switched door {} times with {} wins and {} losses",
+//! total_switches, switch_wins, switch_losses);
+//!
+//! println!("Kept our choice {} times with {} wins and {} losses",
+//! total_keeps, keep_wins, keep_losses);
+//!
+//! // With a large number of simulations, the values should converge to
+//! // 0.667 and 0.333 respectively.
+//! println!("Estimated chance to win if we switch: {}",
+//! switch_wins as f32 / total_switches as f32);
+//! println!("Estimated chance to win if we don't: {}",
+//! keep_wins as f32 / total_keeps as f32);
+//! }
+//! ```
+
+#![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png",
+ html_favicon_url = "https://www.rust-lang.org/favicon.ico",
+ html_root_url = "https://docs.rs/rand/0.4")]
+
+#![deny(missing_debug_implementations)]
+
+#![cfg_attr(not(feature="std"), no_std)]
+#![cfg_attr(all(feature="alloc", not(feature="std")), feature(alloc))]
+#![cfg_attr(feature = "i128_support", feature(i128_type, i128))]
+
+#[cfg(feature="std")] extern crate std as core;
+#[cfg(all(feature = "alloc", not(feature="std")))] extern crate alloc;
+
+use core::marker;
+use core::mem;
+#[cfg(feature="std")] use std::cell::RefCell;
+#[cfg(feature="std")] use std::io;
+#[cfg(feature="std")] use std::rc::Rc;
+
+// external rngs
+pub use jitter::JitterRng;
+#[cfg(feature="std")] pub use os::OsRng;
+
+// pseudo rngs
+pub use isaac::{IsaacRng, Isaac64Rng};
+pub use chacha::ChaChaRng;
+pub use prng::XorShiftRng;
+
+// local use declarations
+#[cfg(target_pointer_width = "32")]
+use prng::IsaacRng as IsaacWordRng;
+#[cfg(target_pointer_width = "64")]
+use prng::Isaac64Rng as IsaacWordRng;
+
+use distributions::{Range, IndependentSample};
+use distributions::range::SampleRange;
+
+// public modules
+pub mod distributions;
+pub mod jitter;
+#[cfg(feature="std")] pub mod os;
+#[cfg(feature="std")] pub mod read;
+pub mod reseeding;
+#[cfg(any(feature="std", feature = "alloc"))] pub mod seq;
+
+// These tiny modules are here to avoid API breakage, probably only temporarily
+pub mod chacha {
+ //! The ChaCha random number generator.
+ pub use prng::ChaChaRng;
+}
+pub mod isaac {
+ //! The ISAAC random number generator.
+ pub use prng::{IsaacRng, Isaac64Rng};
+}
+
+// private modules
+mod rand_impls;
+mod prng;
+
+
+/// A type that can be randomly generated using an `Rng`.
+///
+/// ## Built-in Implementations
+///
+/// This crate implements `Rand` for various primitive types. Assuming the
+/// provided `Rng` is well-behaved, these implementations generate values with
+/// the following ranges and distributions:
+///
+/// * Integers (`i32`, `u32`, `isize`, `usize`, etc.): Uniformly distributed
+/// over all values of the type.
+/// * `char`: Uniformly distributed over all Unicode scalar values, i.e. all
+/// code points in the range `0...0x10_FFFF`, except for the range
+/// `0xD800...0xDFFF` (the surrogate code points). This includes
+/// unassigned/reserved code points.
+/// * `bool`: Generates `false` or `true`, each with probability 0.5.
+/// * Floating point types (`f32` and `f64`): Uniformly distributed in the
+/// half-open range `[0, 1)`. (The [`Open01`], [`Closed01`], [`Exp1`], and
+/// [`StandardNormal`] wrapper types produce floating point numbers with
+/// alternative ranges or distributions.)
+///
+/// [`Open01`]: struct.Open01.html
+/// [`Closed01`]: struct.Closed01.html
+/// [`Exp1`]: distributions/exponential/struct.Exp1.html
+/// [`StandardNormal`]: distributions/normal/struct.StandardNormal.html
+///
+/// The following aggregate types also implement `Rand` as long as their
+/// component types implement it:
+///
+/// * Tuples and arrays: Each element of the tuple or array is generated
+/// independently, using its own `Rand` implementation.
+/// * `Option<T>`: Returns `None` with probability 0.5; otherwise generates a
+/// random `T` and returns `Some(T)`.
+pub trait Rand : Sized {
+ /// Generates a random instance of this type using the specified source of
+ /// randomness.
+ fn rand<R: Rng>(rng: &mut R) -> Self;
+}
+
+/// A random number generator.
+pub trait Rng {
+ /// Return the next random u32.
+ ///
+ /// This rarely needs to be called directly, prefer `r.gen()` to
+ /// `r.next_u32()`.
+ // FIXME #rust-lang/rfcs#628: Should be implemented in terms of next_u64
+ fn next_u32(&mut self) -> u32;
+
+ /// Return the next random u64.
+ ///
+ /// By default this is implemented in terms of `next_u32`. An
+ /// implementation of this trait must provide at least one of
+ /// these two methods. Similarly to `next_u32`, this rarely needs
+ /// to be called directly, prefer `r.gen()` to `r.next_u64()`.
+ fn next_u64(&mut self) -> u64 {
+ ((self.next_u32() as u64) << 32) | (self.next_u32() as u64)
+ }
+
+ /// Return the next random f32 selected from the half-open
+ /// interval `[0, 1)`.
+ ///
+ /// This uses a technique described by Saito and Matsumoto at
+ /// MCQMC'08. Given that the IEEE floating point numbers are
+ /// uniformly distributed over [1,2), we generate a number in
+ /// this range and then offset it onto the range [0,1). Our
+ /// choice of bits (masking v. shifting) is arbitrary and
+ /// should be immaterial for high quality generators. For low
+ /// quality generators (ex. LCG), prefer bitshifting due to
+ /// correlation between sequential low order bits.
+ ///
+ /// See:
+ /// A PRNG specialized in double precision floating point numbers using
+ /// an affine transition
+ ///
+ /// * <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/ARTICLES/dSFMT.pdf>
+ /// * <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/dSFMT-slide-e.pdf>
+ ///
+ /// By default this is implemented in terms of `next_u32`, but a
+ /// random number generator which can generate numbers satisfying
+ /// the requirements directly can overload this for performance.
+ /// It is required that the return value lies in `[0, 1)`.
+ ///
+ /// See `Closed01` for the closed interval `[0,1]`, and
+ /// `Open01` for the open interval `(0,1)`.
+ fn next_f32(&mut self) -> f32 {
+ const UPPER_MASK: u32 = 0x3F800000;
+ const LOWER_MASK: u32 = 0x7FFFFF;
+ let tmp = UPPER_MASK | (self.next_u32() & LOWER_MASK);
+ let result: f32 = unsafe { mem::transmute(tmp) };
+ result - 1.0
+ }
+
+ /// Return the next random f64 selected from the half-open
+ /// interval `[0, 1)`.
+ ///
+ /// By default this is implemented in terms of `next_u64`, but a
+ /// random number generator which can generate numbers satisfying
+ /// the requirements directly can overload this for performance.
+ /// It is required that the return value lies in `[0, 1)`.
+ ///
+ /// See `Closed01` for the closed interval `[0,1]`, and
+ /// `Open01` for the open interval `(0,1)`.
+ fn next_f64(&mut self) -> f64 {
+ const UPPER_MASK: u64 = 0x3FF0000000000000;
+ const LOWER_MASK: u64 = 0xFFFFFFFFFFFFF;
+ let tmp = UPPER_MASK | (self.next_u64() & LOWER_MASK);
+ let result: f64 = unsafe { mem::transmute(tmp) };
+ result - 1.0
+ }
+
+ /// Fill `dest` with random data.
+ ///
+ /// This has a default implementation in terms of `next_u64` and
+ /// `next_u32`, but should be overridden by implementations that
+ /// offer a more efficient solution than just calling those
+ /// methods repeatedly.
+ ///
+ /// This method does *not* have a requirement to bear any fixed
+ /// relationship to the other methods, for example, it does *not*
+ /// have to result in the same output as progressively filling
+ /// `dest` with `self.gen::<u8>()`, and any such behaviour should
+ /// not be relied upon.
+ ///
+ /// This method should guarantee that `dest` is entirely filled
+ /// with new data, and may panic if this is impossible
+ /// (e.g. reading past the end of a file that is being used as the
+ /// source of randomness).
+ ///
+ /// # Example
+ ///
+ /// ```rust
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let mut v = [0u8; 13579];
+ /// thread_rng().fill_bytes(&mut v);
+ /// println!("{:?}", &v[..]);
+ /// ```
+ fn fill_bytes(&mut self, dest: &mut [u8]) {
+ // this could, in theory, be done by transmuting dest to a
+ // [u64], but this is (1) likely to be undefined behaviour for
+ // LLVM, (2) has to be very careful about alignment concerns,
+ // (3) adds more `unsafe` that needs to be checked, (4)
+ // probably doesn't give much performance gain if
+ // optimisations are on.
+ let mut count = 0;
+ let mut num = 0;
+ for byte in dest.iter_mut() {
+ if count == 0 {
+ // we could micro-optimise here by generating a u32 if
+ // we only need a few more bytes to fill the vector
+ // (i.e. at most 4).
+ num = self.next_u64();
+ count = 8;
+ }
+
+ *byte = (num & 0xff) as u8;
+ num >>= 8;
+ count -= 1;
+ }
+ }
+
+ /// Return a random value of a `Rand` type.
+ ///
+ /// # Example
+ ///
+ /// ```rust
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let mut rng = thread_rng();
+ /// let x: u32 = rng.gen();
+ /// println!("{}", x);
+ /// println!("{:?}", rng.gen::<(f64, bool)>());
+ /// ```
+ #[inline(always)]
+ fn gen<T: Rand>(&mut self) -> T where Self: Sized {
+ Rand::rand(self)
+ }
+
+ /// Return an iterator that will yield an infinite number of randomly
+ /// generated items.
+ ///
+ /// # Example
+ ///
+ /// ```
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let mut rng = thread_rng();
+ /// let x = rng.gen_iter::<u32>().take(10).collect::<Vec<u32>>();
+ /// println!("{:?}", x);
+ /// println!("{:?}", rng.gen_iter::<(f64, bool)>().take(5)
+ /// .collect::<Vec<(f64, bool)>>());
+ /// ```
+ fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> where Self: Sized {
+ Generator { rng: self, _marker: marker::PhantomData }
+ }
+
+ /// Generate a random value in the range [`low`, `high`).
+ ///
+ /// This is a convenience wrapper around
+ /// `distributions::Range`. If this function will be called
+ /// repeatedly with the same arguments, one should use `Range`, as
+ /// that will amortize the computations that allow for perfect
+ /// uniformity, as they only happen on initialization.
+ ///
+ /// # Panics
+ ///
+ /// Panics if `low >= high`.
+ ///
+ /// # Example
+ ///
+ /// ```rust
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let mut rng = thread_rng();
+ /// let n: u32 = rng.gen_range(0, 10);
+ /// println!("{}", n);
+ /// let m: f64 = rng.gen_range(-40.0f64, 1.3e5f64);
+ /// println!("{}", m);
+ /// ```
+ fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T where Self: Sized {
+ assert!(low < high, "Rng.gen_range called with low >= high");
+ Range::new(low, high).ind_sample(self)
+ }
+
+ /// Return a bool with a 1 in n chance of true
+ ///
+ /// # Example
+ ///
+ /// ```rust
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let mut rng = thread_rng();
+ /// println!("{}", rng.gen_weighted_bool(3));
+ /// ```
+ fn gen_weighted_bool(&mut self, n: u32) -> bool where Self: Sized {
+ n <= 1 || self.gen_range(0, n) == 0
+ }
+
+ /// Return an iterator of random characters from the set A-Z,a-z,0-9.
+ ///
+ /// # Example
+ ///
+ /// ```rust
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let s: String = thread_rng().gen_ascii_chars().take(10).collect();
+ /// println!("{}", s);
+ /// ```
+ fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> where Self: Sized {
+ AsciiGenerator { rng: self }
+ }
+
+ /// Return a random element from `values`.
+ ///
+ /// Return `None` if `values` is empty.
+ ///
+ /// # Example
+ ///
+ /// ```
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let choices = [1, 2, 4, 8, 16, 32];
+ /// let mut rng = thread_rng();
+ /// println!("{:?}", rng.choose(&choices));
+ /// assert_eq!(rng.choose(&choices[..0]), None);
+ /// ```
+ fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> where Self: Sized {
+ if values.is_empty() {
+ None
+ } else {
+ Some(&values[self.gen_range(0, values.len())])
+ }
+ }
+
+ /// Return a mutable pointer to a random element from `values`.
+ ///
+ /// Return `None` if `values` is empty.
+ fn choose_mut<'a, T>(&mut self, values: &'a mut [T]) -> Option<&'a mut T> where Self: Sized {
+ if values.is_empty() {
+ None
+ } else {
+ let len = values.len();
+ Some(&mut values[self.gen_range(0, len)])
+ }
+ }
+
+ /// Shuffle a mutable slice in place.
+ ///
+ /// This applies Durstenfeld's algorithm for the [Fisher–Yates shuffle](https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm)
+ /// which produces an unbiased permutation.
+ ///
+ /// # Example
+ ///
+ /// ```rust
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let mut rng = thread_rng();
+ /// let mut y = [1, 2, 3];
+ /// rng.shuffle(&mut y);
+ /// println!("{:?}", y);
+ /// rng.shuffle(&mut y);
+ /// println!("{:?}", y);
+ /// ```
+ fn shuffle<T>(&mut self, values: &mut [T]) where Self: Sized {
+ let mut i = values.len();
+ while i >= 2 {
+ // invariant: elements with index >= i have been locked in place.
+ i -= 1;
+ // lock element i in place.
+ values.swap(i, self.gen_range(0, i + 1));
+ }
+ }
+}
+
+impl<'a, R: ?Sized> Rng for &'a mut R where R: Rng {
+ fn next_u32(&mut self) -> u32 {
+ (**self).next_u32()
+ }
+
+ fn next_u64(&mut self) -> u64 {
+ (**self).next_u64()
+ }
+
+ fn next_f32(&mut self) -> f32 {
+ (**self).next_f32()
+ }
+
+ fn next_f64(&mut self) -> f64 {
+ (**self).next_f64()
+ }
+
+ fn fill_bytes(&mut self, dest: &mut [u8]) {
+ (**self).fill_bytes(dest)
+ }
+}
+
+#[cfg(feature="std")]
+impl<R: ?Sized> Rng for Box<R> where R: Rng {
+ fn next_u32(&mut self) -> u32 {
+ (**self).next_u32()
+ }
+
+ fn next_u64(&mut self) -> u64 {
+ (**self).next_u64()
+ }
+
+ fn next_f32(&mut self) -> f32 {
+ (**self).next_f32()
+ }
+
+ fn next_f64(&mut self) -> f64 {
+ (**self).next_f64()
+ }
+
+ fn fill_bytes(&mut self, dest: &mut [u8]) {
+ (**self).fill_bytes(dest)
+ }
+}
+
+/// Iterator which will generate a stream of random items.
+///
+/// This iterator is created via the [`gen_iter`] method on [`Rng`].
+///
+/// [`gen_iter`]: trait.Rng.html#method.gen_iter
+/// [`Rng`]: trait.Rng.html
+#[derive(Debug)]
+pub struct Generator<'a, T, R:'a> {
+ rng: &'a mut R,
+ _marker: marker::PhantomData<fn() -> T>,
+}
+
+impl<'a, T: Rand, R: Rng> Iterator for Generator<'a, T, R> {
+ type Item = T;
+
+ fn next(&mut self) -> Option<T> {
+ Some(self.rng.gen())
+ }
+}
+
+/// Iterator which will continuously generate random ascii characters.
+///
+/// This iterator is created via the [`gen_ascii_chars`] method on [`Rng`].
+///
+/// [`gen_ascii_chars`]: trait.Rng.html#method.gen_ascii_chars
+/// [`Rng`]: trait.Rng.html
+#[derive(Debug)]
+pub struct AsciiGenerator<'a, R:'a> {
+ rng: &'a mut R,
+}
+
+impl<'a, R: Rng> Iterator for AsciiGenerator<'a, R> {
+ type Item = char;
+
+ fn next(&mut self) -> Option<char> {
+ const GEN_ASCII_STR_CHARSET: &'static [u8] =
+ b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
+ abcdefghijklmnopqrstuvwxyz\
+ 0123456789";
+ Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char)
+ }
+}
+
+/// A random number generator that can be explicitly seeded to produce
+/// the same stream of randomness multiple times.
+pub trait SeedableRng<Seed>: Rng {
+ /// Reseed an RNG with the given seed.
+ ///
+ /// # Example
+ ///
+ /// ```rust
+ /// use rand::{Rng, SeedableRng, StdRng};
+ ///
+ /// let seed: &[_] = &[1, 2, 3, 4];
+ /// let mut rng: StdRng = SeedableRng::from_seed(seed);
+ /// println!("{}", rng.gen::<f64>());
+ /// rng.reseed(&[5, 6, 7, 8]);
+ /// println!("{}", rng.gen::<f64>());
+ /// ```
+ fn reseed(&mut self, Seed);
+
+ /// Create a new RNG with the given seed.
+ ///
+ /// # Example
+ ///
+ /// ```rust
+ /// use rand::{Rng, SeedableRng, StdRng};
+ ///
+ /// let seed: &[_] = &[1, 2, 3, 4];
+ /// let mut rng: StdRng = SeedableRng::from_seed(seed);
+ /// println!("{}", rng.gen::<f64>());
+ /// ```
+ fn from_seed(seed: Seed) -> Self;
+}
+
+/// A wrapper for generating floating point numbers uniformly in the
+/// open interval `(0,1)` (not including either endpoint).
+///
+/// Use `Closed01` for the closed interval `[0,1]`, and the default
+/// `Rand` implementation for `f32` and `f64` for the half-open
+/// `[0,1)`.
+///
+/// # Example
+/// ```rust
+/// use rand::{random, Open01};
+///
+/// let Open01(val) = random::<Open01<f32>>();
+/// println!("f32 from (0,1): {}", val);
+/// ```
+#[derive(Debug)]
+pub struct Open01<F>(pub F);
+
+/// A wrapper for generating floating point numbers uniformly in the
+/// closed interval `[0,1]` (including both endpoints).
+///
+/// Use `Open01` for the closed interval `(0,1)`, and the default
+/// `Rand` implementation of `f32` and `f64` for the half-open
+/// `[0,1)`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::{random, Closed01};
+///
+/// let Closed01(val) = random::<Closed01<f32>>();
+/// println!("f32 from [0,1]: {}", val);
+/// ```
+#[derive(Debug)]
+pub struct Closed01<F>(pub F);
+
+/// The standard RNG. This is designed to be efficient on the current
+/// platform.
+#[derive(Copy, Clone, Debug)]
+pub struct StdRng {
+ rng: IsaacWordRng,
+}
+
+impl StdRng {
+ /// Create a randomly seeded instance of `StdRng`.
+ ///
+ /// This is a very expensive operation as it has to read
+ /// randomness from the operating system and use this in an
+ /// expensive seeding operation. If one is only generating a small
+ /// number of random numbers, or doesn't need the utmost speed for
+ /// generating each number, `thread_rng` and/or `random` may be more
+ /// appropriate.
+ ///
+ /// Reading the randomness from the OS may fail, and any error is
+ /// propagated via the `io::Result` return value.
+ #[cfg(feature="std")]
+ pub fn new() -> io::Result<StdRng> {
+ match OsRng::new() {
+ Ok(mut r) => Ok(StdRng { rng: r.gen() }),
+ Err(e1) => {
+ match JitterRng::new() {
+ Ok(mut r) => Ok(StdRng { rng: r.gen() }),
+ Err(_) => {
+ Err(e1)
+ }
+ }
+ }
+ }
+ }
+}
+
+impl Rng for StdRng {
+ #[inline]
+ fn next_u32(&mut self) -> u32 {
+ self.rng.next_u32()
+ }
+
+ #[inline]
+ fn next_u64(&mut self) -> u64 {
+ self.rng.next_u64()
+ }
+}
+
+impl<'a> SeedableRng<&'a [usize]> for StdRng {
+ fn reseed(&mut self, seed: &'a [usize]) {
+ // the internal RNG can just be seeded from the above
+ // randomness.
+ self.rng.reseed(unsafe {mem::transmute(seed)})
+ }
+
+ fn from_seed(seed: &'a [usize]) -> StdRng {
+ StdRng { rng: SeedableRng::from_seed(unsafe {mem::transmute(seed)}) }
+ }
+}
+
+/// Create a weak random number generator with a default algorithm and seed.
+///
+/// It returns the fastest `Rng` algorithm currently available in Rust without
+/// consideration for cryptography or security. If you require a specifically
+/// seeded `Rng` for consistency over time you should pick one algorithm and
+/// create the `Rng` yourself.
+///
+/// This will seed the generator with randomness from thread_rng.
+#[cfg(feature="std")]
+pub fn weak_rng() -> XorShiftRng {
+ thread_rng().gen()
+}
+
+/// Controls how the thread-local RNG is reseeded.
+#[cfg(feature="std")]
+#[derive(Debug)]
+struct ThreadRngReseeder;
+
+#[cfg(feature="std")]
+impl reseeding::Reseeder<StdRng> for ThreadRngReseeder {
+ fn reseed(&mut self, rng: &mut StdRng) {
+ match StdRng::new() {
+ Ok(r) => *rng = r,
+ Err(e) => panic!("No entropy available: {}", e),
+ }
+ }
+}
+#[cfg(feature="std")]
+const THREAD_RNG_RESEED_THRESHOLD: u64 = 32_768;
+#[cfg(feature="std")]
+type ThreadRngInner = reseeding::ReseedingRng<StdRng, ThreadRngReseeder>;
+
+/// The thread-local RNG.
+#[cfg(feature="std")]
+#[derive(Clone, Debug)]
+pub struct ThreadRng {
+ rng: Rc<RefCell<ThreadRngInner>>,
+}
+
+/// Retrieve the lazily-initialized thread-local random number
+/// generator, seeded by the system. Intended to be used in method
+/// chaining style, e.g. `thread_rng().gen::<i32>()`.
+///
+/// After generating a certain amount of randomness, the RNG will reseed itself
+/// from the operating system or, if the operating system RNG returns an error,
+/// a seed based on the current system time.
+///
+/// The internal RNG used is platform and architecture dependent, even
+/// if the operating system random number generator is rigged to give
+/// the same sequence always. If absolute consistency is required,
+/// explicitly select an RNG, e.g. `IsaacRng` or `Isaac64Rng`.
+#[cfg(feature="std")]
+pub fn thread_rng() -> ThreadRng {
+ // used to make space in TLS for a random number generator
+ thread_local!(static THREAD_RNG_KEY: Rc<RefCell<ThreadRngInner>> = {
+ let r = match StdRng::new() {
+ Ok(r) => r,
+ Err(e) => panic!("No entropy available: {}", e),
+ };
+ let rng = reseeding::ReseedingRng::new(r,
+ THREAD_RNG_RESEED_THRESHOLD,
+ ThreadRngReseeder);
+ Rc::new(RefCell::new(rng))
+ });
+
+ ThreadRng { rng: THREAD_RNG_KEY.with(|t| t.clone()) }
+}
+
+#[cfg(feature="std")]
+impl Rng for ThreadRng {
+ fn next_u32(&mut self) -> u32 {
+ self.rng.borrow_mut().next_u32()
+ }
+
+ fn next_u64(&mut self) -> u64 {
+ self.rng.borrow_mut().next_u64()
+ }
+
+ #[inline]
+ fn fill_bytes(&mut self, bytes: &mut [u8]) {
+ self.rng.borrow_mut().fill_bytes(bytes)
+ }
+}
+
+/// Generates a random value using the thread-local random number generator.
+///
+/// `random()` can generate various types of random things, and so may require
+/// type hinting to generate the specific type you want.
+///
+/// This function uses the thread local random number generator. This means
+/// that if you're calling `random()` in a loop, caching the generator can
+/// increase performance. An example is shown below.
+///
+/// # Examples
+///
+/// ```
+/// let x = rand::random::<u8>();
+/// println!("{}", x);
+///
+/// let y = rand::random::<f64>();
+/// println!("{}", y);
+///
+/// if rand::random() { // generates a boolean
+/// println!("Better lucky than good!");
+/// }
+/// ```
+///
+/// Caching the thread local random number generator:
+///
+/// ```
+/// use rand::Rng;
+///
+/// let mut v = vec![1, 2, 3];
+///
+/// for x in v.iter_mut() {
+/// *x = rand::random()
+/// }
+///
+/// // can be made faster by caching thread_rng
+///
+/// let mut rng = rand::thread_rng();
+///
+/// for x in v.iter_mut() {
+/// *x = rng.gen();
+/// }
+/// ```
+#[cfg(feature="std")]
+#[inline]
+pub fn random<T: Rand>() -> T {
+ thread_rng().gen()
+}
+
+/// DEPRECATED: use `seq::sample_iter` instead.
+///
+/// Randomly sample up to `amount` elements from a finite iterator.
+/// The order of elements in the sample is not random.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::{thread_rng, sample};
+///
+/// let mut rng = thread_rng();
+/// let sample = sample(&mut rng, 1..100, 5);
+/// println!("{:?}", sample);
+/// ```
+#[cfg(feature="std")]
+#[inline(always)]
+#[deprecated(since="0.4.0", note="renamed to seq::sample_iter")]
+pub fn sample<T, I, R>(rng: &mut R, iterable: I, amount: usize) -> Vec<T>
+ where I: IntoIterator<Item=T>,
+ R: Rng,
+{
+ // the legacy sample didn't care whether amount was met
+ seq::sample_iter(rng, iterable, amount)
+ .unwrap_or_else(|e| e)
+}
+
+#[cfg(test)]
+mod test {
+ use super::{Rng, thread_rng, random, SeedableRng, StdRng, weak_rng};
+ use std::iter::repeat;
+
+ pub struct MyRng<R> { inner: R }
+
+ impl<R: Rng> Rng for MyRng<R> {
+ fn next_u32(&mut self) -> u32 {
+ fn next<T: Rng>(t: &mut T) -> u32 {
+ t.next_u32()
+ }
+ next(&mut self.inner)
+ }
+ }
+
+ pub fn rng() -> MyRng<::ThreadRng> {
+ MyRng { inner: ::thread_rng() }
+ }
+
+ struct ConstRng { i: u64 }
+ impl Rng for ConstRng {
+ fn next_u32(&mut self) -> u32 { self.i as u32 }
+ fn next_u64(&mut self) -> u64 { self.i }
+
+ // no fill_bytes on purpose
+ }
+
+ pub fn iter_eq<I, J>(i: I, j: J) -> bool
+ where I: IntoIterator,
+ J: IntoIterator<Item=I::Item>,
+ I::Item: Eq
+ {
+ // make sure the iterators have equal length
+ let mut i = i.into_iter();
+ let mut j = j.into_iter();
+ loop {
+ match (i.next(), j.next()) {
+ (Some(ref ei), Some(ref ej)) if ei == ej => { }
+ (None, None) => return true,
+ _ => return false,
+ }
+ }
+ }
+
+ #[test]
+ fn test_fill_bytes_default() {
+ let mut r = ConstRng { i: 0x11_22_33_44_55_66_77_88 };
+
+ // check every remainder mod 8, both in small and big vectors.
+ let lengths = [0, 1, 2, 3, 4, 5, 6, 7,
+ 80, 81, 82, 83, 84, 85, 86, 87];
+ for &n in lengths.iter() {
+ let mut v = repeat(0u8).take(n).collect::<Vec<_>>();
+ r.fill_bytes(&mut v);
+
+ // use this to get nicer error messages.
+ for (i, &byte) in v.iter().enumerate() {
+ if byte == 0 {
+ panic!("byte {} of {} is zero", i, n)
+ }
+ }
+ }
+ }
+
+ #[test]
+ fn test_gen_range() {
+ let mut r = thread_rng();
+ for _ in 0..1000 {
+ let a = r.gen_range(-3, 42);
+ assert!(a >= -3 && a < 42);
+ assert_eq!(r.gen_range(0, 1), 0);
+ assert_eq!(r.gen_range(-12, -11), -12);
+ }
+
+ for _ in 0..1000 {
+ let a = r.gen_range(10, 42);
+ assert!(a >= 10 && a < 42);
+ assert_eq!(r.gen_range(0, 1), 0);
+ assert_eq!(r.gen_range(3_000_000, 3_000_001), 3_000_000);
+ }
+
+ }
+
+ #[test]
+ #[should_panic]
+ fn test_gen_range_panic_int() {
+ let mut r = thread_rng();
+ r.gen_range(5, -2);
+ }
+
+ #[test]
+ #[should_panic]
+ fn test_gen_range_panic_usize() {
+ let mut r = thread_rng();
+ r.gen_range(5, 2);
+ }
+
+ #[test]
+ fn test_gen_weighted_bool() {
+ let mut r = thread_rng();
+ assert_eq!(r.gen_weighted_bool(0), true);
+ assert_eq!(r.gen_weighted_bool(1), true);
+ }
+
+ #[test]
+ fn test_gen_ascii_str() {
+ let mut r = thread_rng();
+ assert_eq!(r.gen_ascii_chars().take(0).count(), 0);
+ assert_eq!(r.gen_ascii_chars().take(10).count(), 10);
+ assert_eq!(r.gen_ascii_chars().take(16).count(), 16);
+ }
+
+ #[test]
+ fn test_gen_vec() {
+ let mut r = thread_rng();
+ assert_eq!(r.gen_iter::<u8>().take(0).count(), 0);
+ assert_eq!(r.gen_iter::<u8>().take(10).count(), 10);
+ assert_eq!(r.gen_iter::<f64>().take(16).count(), 16);
+ }
+
+ #[test]
+ fn test_choose() {
+ let mut r = thread_rng();
+ assert_eq!(r.choose(&[1, 1, 1]).map(|&x|x), Some(1));
+
+ let v: &[isize] = &[];
+ assert_eq!(r.choose(v), None);
+ }
+
+ #[test]
+ fn test_shuffle() {
+ let mut r = thread_rng();
+ let empty: &mut [isize] = &mut [];
+ r.shuffle(empty);
+ let mut one = [1];
+ r.shuffle(&mut one);
+ let b: &[_] = &[1];
+ assert_eq!(one, b);
+
+ let mut two = [1, 2];
+ r.shuffle(&mut two);
+ assert!(two == [1, 2] || two == [2, 1]);
+
+ let mut x = [1, 1, 1];
+ r.shuffle(&mut x);
+ let b: &[_] = &[1, 1, 1];
+ assert_eq!(x, b);
+ }
+
+ #[test]
+ fn test_thread_rng() {
+ let mut r = thread_rng();
+ r.gen::<i32>();
+ let mut v = [1, 1, 1];
+ r.shuffle(&mut v);
+ let b: &[_] = &[1, 1, 1];
+ assert_eq!(v, b);
+ assert_eq!(r.gen_range(0, 1), 0);
+ }
+
+ #[test]
+ fn test_rng_trait_object() {
+ let mut rng = thread_rng();
+ {
+ let mut r = &mut rng as &mut Rng;
+ r.next_u32();
+ (&mut r).gen::<i32>();
+ let mut v = [1, 1, 1];
+ (&mut r).shuffle(&mut v);
+ let b: &[_] = &[1, 1, 1];
+ assert_eq!(v, b);
+ assert_eq!((&mut r).gen_range(0, 1), 0);
+ }
+ {
+ let mut r = Box::new(rng) as Box<Rng>;
+ r.next_u32();
+ r.gen::<i32>();
+ let mut v = [1, 1, 1];
+ r.shuffle(&mut v);
+ let b: &[_] = &[1, 1, 1];
+ assert_eq!(v, b);
+ assert_eq!(r.gen_range(0, 1), 0);
+ }
+ }
+
+ #[test]
+ fn test_random() {
+ // not sure how to test this aside from just getting some values
+ let _n : usize = random();
+ let _f : f32 = random();
+ let _o : Option<Option<i8>> = random();
+ let _many : ((),
+ (usize,
+ isize,
+ Option<(u32, (bool,))>),
+ (u8, i8, u16, i16, u32, i32, u64, i64),
+ (f32, (f64, (f64,)))) = random();
+ }
+
+ #[test]
+ fn test_std_rng_seeded() {
+ let s = thread_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>();
+ let mut ra: StdRng = SeedableRng::from_seed(&s[..]);
+ let mut rb: StdRng = SeedableRng::from_seed(&s[..]);
+ assert!(iter_eq(ra.gen_ascii_chars().take(100),
+ rb.gen_ascii_chars().take(100)));
+ }
+
+ #[test]
+ fn test_std_rng_reseed() {
+ let s = thread_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>();
+ let mut r: StdRng = SeedableRng::from_seed(&s[..]);
+ let string1 = r.gen_ascii_chars().take(100).collect::<String>();
+
+ r.reseed(&s);
+
+ let string2 = r.gen_ascii_chars().take(100).collect::<String>();
+ assert_eq!(string1, string2);
+ }
+
+ #[test]
+ fn test_weak_rng() {
+ let s = weak_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>();
+ let mut ra: StdRng = SeedableRng::from_seed(&s[..]);
+ let mut rb: StdRng = SeedableRng::from_seed(&s[..]);
+ assert!(iter_eq(ra.gen_ascii_chars().take(100),
+ rb.gen_ascii_chars().take(100)));
+ }
+}
diff --git a/rand/src/os.rs b/rand/src/os.rs
new file mode 100644
index 0000000..10022fb
--- /dev/null
+++ b/rand/src/os.rs
@@ -0,0 +1,617 @@
+// Copyright 2013-2015 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.
+
+//! Interfaces to the operating system provided random number
+//! generators.
+
+use std::{io, mem, fmt};
+use Rng;
+
+/// A random number generator that retrieves randomness straight from
+/// the operating system. Platform sources:
+///
+/// - Unix-like systems (Linux, Android, Mac OSX): read directly from
+/// `/dev/urandom`, or from `getrandom(2)` system call if available.
+/// - OpenBSD: calls `getentropy(2)`
+/// - FreeBSD: uses the `kern.arandom` `sysctl(2)` mib
+/// - Windows: calls `RtlGenRandom`, exported from `advapi32.dll` as
+/// `SystemFunction036`.
+/// - iOS: calls SecRandomCopyBytes as /dev/(u)random is sandboxed.
+/// - PNaCl: calls into the `nacl-irt-random-0.1` IRT interface.
+///
+/// This usually does not block. On some systems (e.g. FreeBSD, OpenBSD,
+/// Max OS X, and modern Linux) this may block very early in the init
+/// process, if the CSPRNG has not been seeded yet.[1]
+///
+/// [1] See <https://www.python.org/dev/peps/pep-0524/> for a more
+/// in-depth discussion.
+pub struct OsRng(imp::OsRng);
+
+impl OsRng {
+ /// Create a new `OsRng`.
+ pub fn new() -> io::Result<OsRng> {
+ imp::OsRng::new().map(OsRng)
+ }
+}
+
+impl Rng for OsRng {
+ fn next_u32(&mut self) -> u32 { self.0.next_u32() }
+ fn next_u64(&mut self) -> u64 { self.0.next_u64() }
+ fn fill_bytes(&mut self, v: &mut [u8]) { self.0.fill_bytes(v) }
+}
+
+impl fmt::Debug for OsRng {
+ fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+ write!(f, "OsRng {{}}")
+ }
+}
+
+fn next_u32(fill_buf: &mut FnMut(&mut [u8])) -> u32 {
+ let mut buf: [u8; 4] = [0; 4];
+ fill_buf(&mut buf);
+ unsafe { mem::transmute::<[u8; 4], u32>(buf) }
+}
+
+fn next_u64(fill_buf: &mut FnMut(&mut [u8])) -> u64 {
+ let mut buf: [u8; 8] = [0; 8];
+ fill_buf(&mut buf);
+ unsafe { mem::transmute::<[u8; 8], u64>(buf) }
+}
+
+#[cfg(all(unix, not(target_os = "ios"),
+ not(target_os = "nacl"),
+ not(target_os = "freebsd"),
+ not(target_os = "fuchsia"),
+ not(target_os = "openbsd"),
+ not(target_os = "redox")))]
+mod imp {
+ extern crate libc;
+
+ use super::{next_u32, next_u64};
+ use self::OsRngInner::*;
+
+ use std::io;
+ use std::fs::File;
+ use Rng;
+ use read::ReadRng;
+
+ #[cfg(all(target_os = "linux",
+ any(target_arch = "x86_64",
+ target_arch = "x86",
+ target_arch = "arm",
+ target_arch = "aarch64",
+ target_arch = "powerpc")))]
+ fn getrandom(buf: &mut [u8]) -> libc::c_long {
+ extern "C" {
+ fn syscall(number: libc::c_long, ...) -> libc::c_long;
+ }
+
+ #[cfg(target_arch = "x86_64")]
+ const NR_GETRANDOM: libc::c_long = 318;
+ #[cfg(target_arch = "x86")]
+ const NR_GETRANDOM: libc::c_long = 355;
+ #[cfg(target_arch = "arm")]
+ const NR_GETRANDOM: libc::c_long = 384;
+ #[cfg(target_arch = "aarch64")]
+ const NR_GETRANDOM: libc::c_long = 278;
+ #[cfg(target_arch = "powerpc")]
+ const NR_GETRANDOM: libc::c_long = 359;
+
+ unsafe {
+ syscall(NR_GETRANDOM, buf.as_mut_ptr(), buf.len(), 0)
+ }
+ }
+
+ #[cfg(not(all(target_os = "linux",
+ any(target_arch = "x86_64",
+ target_arch = "x86",
+ target_arch = "arm",
+ target_arch = "aarch64",
+ target_arch = "powerpc"))))]
+ fn getrandom(_buf: &mut [u8]) -> libc::c_long { -1 }
+
+ fn getrandom_fill_bytes(v: &mut [u8]) {
+ let mut read = 0;
+ let len = v.len();
+ while read < len {
+ let result = getrandom(&mut v[read..]);
+ if result == -1 {
+ let err = io::Error::last_os_error();
+ if err.kind() == io::ErrorKind::Interrupted {
+ continue
+ } else {
+ panic!("unexpected getrandom error: {}", err);
+ }
+ } else {
+ read += result as usize;
+ }
+ }
+ }
+
+ #[cfg(all(target_os = "linux",
+ any(target_arch = "x86_64",
+ target_arch = "x86",
+ target_arch = "arm",
+ target_arch = "aarch64",
+ target_arch = "powerpc")))]
+ fn is_getrandom_available() -> bool {
+ use std::sync::atomic::{AtomicBool, ATOMIC_BOOL_INIT, Ordering};
+ use std::sync::{Once, ONCE_INIT};
+
+ static CHECKER: Once = ONCE_INIT;
+ static AVAILABLE: AtomicBool = ATOMIC_BOOL_INIT;
+
+ CHECKER.call_once(|| {
+ let mut buf: [u8; 0] = [];
+ let result = getrandom(&mut buf);
+ let available = if result == -1 {
+ let err = io::Error::last_os_error().raw_os_error();
+ err != Some(libc::ENOSYS)
+ } else {
+ true
+ };
+ AVAILABLE.store(available, Ordering::Relaxed);
+ });
+
+ AVAILABLE.load(Ordering::Relaxed)
+ }
+
+ #[cfg(not(all(target_os = "linux",
+ any(target_arch = "x86_64",
+ target_arch = "x86",
+ target_arch = "arm",
+ target_arch = "aarch64",
+ target_arch = "powerpc"))))]
+ fn is_getrandom_available() -> bool { false }
+
+ pub struct OsRng {
+ inner: OsRngInner,
+ }
+
+ enum OsRngInner {
+ OsGetrandomRng,
+ OsReadRng(ReadRng<File>),
+ }
+
+ impl OsRng {
+ pub fn new() -> io::Result<OsRng> {
+ if is_getrandom_available() {
+ return Ok(OsRng { inner: OsGetrandomRng });
+ }
+
+ let reader = try!(File::open("/dev/urandom"));
+ let reader_rng = ReadRng::new(reader);
+
+ Ok(OsRng { inner: OsReadRng(reader_rng) })
+ }
+ }
+
+ impl Rng for OsRng {
+ fn next_u32(&mut self) -> u32 {
+ match self.inner {
+ OsGetrandomRng => next_u32(&mut getrandom_fill_bytes),
+ OsReadRng(ref mut rng) => rng.next_u32(),
+ }
+ }
+ fn next_u64(&mut self) -> u64 {
+ match self.inner {
+ OsGetrandomRng => next_u64(&mut getrandom_fill_bytes),
+ OsReadRng(ref mut rng) => rng.next_u64(),
+ }
+ }
+ fn fill_bytes(&mut self, v: &mut [u8]) {
+ match self.inner {
+ OsGetrandomRng => getrandom_fill_bytes(v),
+ OsReadRng(ref mut rng) => rng.fill_bytes(v)
+ }
+ }
+ }
+}
+
+#[cfg(target_os = "ios")]
+mod imp {
+ extern crate libc;
+
+ use super::{next_u32, next_u64};
+
+ use std::io;
+ use Rng;
+ use self::libc::{c_int, size_t};
+
+ #[derive(Debug)]
+ pub struct OsRng;
+
+ enum SecRandom {}
+
+ #[allow(non_upper_case_globals)]
+ const kSecRandomDefault: *const SecRandom = 0 as *const SecRandom;
+
+ #[link(name = "Security", kind = "framework")]
+ extern {
+ fn SecRandomCopyBytes(rnd: *const SecRandom,
+ count: size_t, bytes: *mut u8) -> c_int;
+ }
+
+ impl OsRng {
+ pub fn new() -> io::Result<OsRng> {
+ Ok(OsRng)
+ }
+ }
+
+ impl Rng for OsRng {
+ fn next_u32(&mut self) -> u32 {
+ next_u32(&mut |v| self.fill_bytes(v))
+ }
+ fn next_u64(&mut self) -> u64 {
+ next_u64(&mut |v| self.fill_bytes(v))
+ }
+ fn fill_bytes(&mut self, v: &mut [u8]) {
+ let ret = unsafe {
+ SecRandomCopyBytes(kSecRandomDefault, v.len() as size_t, v.as_mut_ptr())
+ };
+ if ret == -1 {
+ panic!("couldn't generate random bytes: {}", io::Error::last_os_error());
+ }
+ }
+ }
+}
+
+#[cfg(target_os = "freebsd")]
+mod imp {
+ extern crate libc;
+
+ use std::{io, ptr};
+ use Rng;
+
+ use super::{next_u32, next_u64};
+
+ #[derive(Debug)]
+ pub struct OsRng;
+
+ impl OsRng {
+ pub fn new() -> io::Result<OsRng> {
+ Ok(OsRng)
+ }
+ }
+
+ impl Rng for OsRng {
+ fn next_u32(&mut self) -> u32 {
+ next_u32(&mut |v| self.fill_bytes(v))
+ }
+ fn next_u64(&mut self) -> u64 {
+ next_u64(&mut |v| self.fill_bytes(v))
+ }
+ fn fill_bytes(&mut self, v: &mut [u8]) {
+ let mib = [libc::CTL_KERN, libc::KERN_ARND];
+ // kern.arandom permits a maximum buffer size of 256 bytes
+ for s in v.chunks_mut(256) {
+ let mut s_len = s.len();
+ let ret = unsafe {
+ libc::sysctl(mib.as_ptr(), mib.len() as libc::c_uint,
+ s.as_mut_ptr() as *mut _, &mut s_len,
+ ptr::null(), 0)
+ };
+ if ret == -1 || s_len != s.len() {
+ panic!("kern.arandom sysctl failed! (returned {}, s.len() {}, oldlenp {})",
+ ret, s.len(), s_len);
+ }
+ }
+ }
+ }
+}
+
+#[cfg(target_os = "openbsd")]
+mod imp {
+ extern crate libc;
+
+ use std::io;
+ use Rng;
+
+ use super::{next_u32, next_u64};
+
+ #[derive(Debug)]
+ pub struct OsRng;
+
+ impl OsRng {
+ pub fn new() -> io::Result<OsRng> {
+ Ok(OsRng)
+ }
+ }
+
+ impl Rng for OsRng {
+ fn next_u32(&mut self) -> u32 {
+ next_u32(&mut |v| self.fill_bytes(v))
+ }
+ fn next_u64(&mut self) -> u64 {
+ next_u64(&mut |v| self.fill_bytes(v))
+ }
+ fn fill_bytes(&mut self, v: &mut [u8]) {
+ // getentropy(2) permits a maximum buffer size of 256 bytes
+ for s in v.chunks_mut(256) {
+ let ret = unsafe {
+ libc::getentropy(s.as_mut_ptr() as *mut libc::c_void, s.len())
+ };
+ if ret == -1 {
+ let err = io::Error::last_os_error();
+ panic!("getentropy failed: {}", err);
+ }
+ }
+ }
+ }
+}
+
+#[cfg(target_os = "redox")]
+mod imp {
+ use std::io;
+ use std::fs::File;
+ use Rng;
+ use read::ReadRng;
+
+ #[derive(Debug)]
+ pub struct OsRng {
+ inner: ReadRng<File>,
+ }
+
+ impl OsRng {
+ pub fn new() -> io::Result<OsRng> {
+ let reader = try!(File::open("rand:"));
+ let reader_rng = ReadRng::new(reader);
+
+ Ok(OsRng { inner: reader_rng })
+ }
+ }
+
+ impl Rng for OsRng {
+ fn next_u32(&mut self) -> u32 {
+ self.inner.next_u32()
+ }
+ fn next_u64(&mut self) -> u64 {
+ self.inner.next_u64()
+ }
+ fn fill_bytes(&mut self, v: &mut [u8]) {
+ self.inner.fill_bytes(v)
+ }
+ }
+}
+
+#[cfg(target_os = "fuchsia")]
+mod imp {
+ extern crate fuchsia_zircon;
+
+ use std::io;
+ use Rng;
+
+ use super::{next_u32, next_u64};
+
+ #[derive(Debug)]
+ pub struct OsRng;
+
+ impl OsRng {
+ pub fn new() -> io::Result<OsRng> {
+ Ok(OsRng)
+ }
+ }
+
+ impl Rng for OsRng {
+ fn next_u32(&mut self) -> u32 {
+ next_u32(&mut |v| self.fill_bytes(v))
+ }
+ fn next_u64(&mut self) -> u64 {
+ next_u64(&mut |v| self.fill_bytes(v))
+ }
+ fn fill_bytes(&mut self, v: &mut [u8]) {
+ for s in v.chunks_mut(fuchsia_zircon::sys::ZX_CPRNG_DRAW_MAX_LEN) {
+ let mut filled = 0;
+ while filled < s.len() {
+ match fuchsia_zircon::cprng_draw(&mut s[filled..]) {
+ Ok(actual) => filled += actual,
+ Err(e) => panic!("cprng_draw failed: {:?}", e),
+ };
+ }
+ }
+ }
+ }
+}
+
+#[cfg(windows)]
+mod imp {
+ extern crate winapi;
+
+ use std::io;
+ use Rng;
+
+ use super::{next_u32, next_u64};
+
+ use self::winapi::shared::minwindef::ULONG;
+ use self::winapi::um::ntsecapi::RtlGenRandom;
+ use self::winapi::um::winnt::PVOID;
+
+ #[derive(Debug)]
+ pub struct OsRng;
+
+ impl OsRng {
+ pub fn new() -> io::Result<OsRng> {
+ Ok(OsRng)
+ }
+ }
+
+ impl Rng for OsRng {
+ fn next_u32(&mut self) -> u32 {
+ next_u32(&mut |v| self.fill_bytes(v))
+ }
+ fn next_u64(&mut self) -> u64 {
+ next_u64(&mut |v| self.fill_bytes(v))
+ }
+ fn fill_bytes(&mut self, v: &mut [u8]) {
+ // RtlGenRandom takes an ULONG (u32) for the length so we need to
+ // split up the buffer.
+ for slice in v.chunks_mut(<ULONG>::max_value() as usize) {
+ let ret = unsafe {
+ RtlGenRandom(slice.as_mut_ptr() as PVOID, slice.len() as ULONG)
+ };
+ if ret == 0 {
+ panic!("couldn't generate random bytes: {}",
+ io::Error::last_os_error());
+ }
+ }
+ }
+ }
+}
+
+#[cfg(target_os = "nacl")]
+mod imp {
+ extern crate libc;
+
+ use std::io;
+ use std::mem;
+ use Rng;
+
+ use super::{next_u32, next_u64};
+
+ #[derive(Debug)]
+ pub struct OsRng(extern fn(dest: *mut libc::c_void,
+ bytes: libc::size_t,
+ read: *mut libc::size_t) -> libc::c_int);
+
+ extern {
+ fn nacl_interface_query(name: *const libc::c_char,
+ table: *mut libc::c_void,
+ table_size: libc::size_t) -> libc::size_t;
+ }
+
+ const INTERFACE: &'static [u8] = b"nacl-irt-random-0.1\0";
+
+ #[repr(C)]
+ struct NaClIRTRandom {
+ get_random_bytes: Option<extern fn(dest: *mut libc::c_void,
+ bytes: libc::size_t,
+ read: *mut libc::size_t) -> libc::c_int>,
+ }
+
+ impl OsRng {
+ pub fn new() -> io::Result<OsRng> {
+ let mut iface = NaClIRTRandom {
+ get_random_bytes: None,
+ };
+ let result = unsafe {
+ nacl_interface_query(INTERFACE.as_ptr() as *const _,
+ mem::transmute(&mut iface),
+ mem::size_of::<NaClIRTRandom>() as libc::size_t)
+ };
+ if result != 0 {
+ assert!(iface.get_random_bytes.is_some());
+ let result = OsRng(iface.get_random_bytes.take().unwrap());
+ Ok(result)
+ } else {
+ let error = io::ErrorKind::NotFound;
+ let error = io::Error::new(error, "IRT random interface missing");
+ Err(error)
+ }
+ }
+ }
+
+ impl Rng for OsRng {
+ fn next_u32(&mut self) -> u32 {
+ next_u32(&mut |v| self.fill_bytes(v))
+ }
+ fn next_u64(&mut self) -> u64 {
+ next_u64(&mut |v| self.fill_bytes(v))
+ }
+ fn fill_bytes(&mut self, v: &mut [u8]) {
+ let mut read = 0;
+ loop {
+ let mut r: libc::size_t = 0;
+ let len = v.len();
+ let error = (self.0)(v[read..].as_mut_ptr() as *mut _,
+ (len - read) as libc::size_t,
+ &mut r as *mut _);
+ assert!(error == 0, "`get_random_bytes` failed!");
+ read += r as usize;
+
+ if read >= v.len() { break; }
+ }
+ }
+ }
+}
+
+#[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))]
+mod imp {
+ use std::io;
+ use Rng;
+
+ #[derive(Debug)]
+ pub struct OsRng;
+
+ impl OsRng {
+ pub fn new() -> io::Result<OsRng> {
+ Err(io::Error::new(io::ErrorKind::Other, "Not supported"))
+ }
+ }
+
+ impl Rng for OsRng {
+ fn next_u32(&mut self) -> u32 {
+ panic!("Not supported")
+ }
+ }
+}
+
+#[cfg(test)]
+mod test {
+ use std::sync::mpsc::channel;
+ use Rng;
+ use OsRng;
+ use std::thread;
+
+ #[test]
+ fn test_os_rng() {
+ let mut r = OsRng::new().unwrap();
+
+ r.next_u32();
+ r.next_u64();
+
+ let mut v = [0u8; 1000];
+ r.fill_bytes(&mut v);
+ }
+
+ #[test]
+ fn test_os_rng_tasks() {
+
+ let mut txs = vec!();
+ for _ in 0..20 {
+ let (tx, rx) = channel();
+ txs.push(tx);
+
+ thread::spawn(move|| {
+ // wait until all the tasks are ready to go.
+ rx.recv().unwrap();
+
+ // deschedule to attempt to interleave things as much
+ // as possible (XXX: is this a good test?)
+ let mut r = OsRng::new().unwrap();
+ thread::yield_now();
+ let mut v = [0u8; 1000];
+
+ for _ in 0..100 {
+ r.next_u32();
+ thread::yield_now();
+ r.next_u64();
+ thread::yield_now();
+ r.fill_bytes(&mut v);
+ thread::yield_now();
+ }
+ });
+ }
+
+ // start all the tasks
+ for tx in txs.iter() {
+ tx.send(()).unwrap();
+ }
+ }
+}
diff --git a/rand/src/prng/chacha.rs b/rand/src/prng/chacha.rs
new file mode 100644
index 0000000..a73e8e7
--- /dev/null
+++ b/rand/src/prng/chacha.rs
@@ -0,0 +1,321 @@
+// Copyright 2014 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 ChaCha random number generator.
+
+use core::num::Wrapping as w;
+use {Rng, SeedableRng, Rand};
+
+#[allow(bad_style)]
+type w32 = w<u32>;
+
+const KEY_WORDS : usize = 8; // 8 words for the 256-bit key
+const STATE_WORDS : usize = 16;
+const CHACHA_ROUNDS: u32 = 20; // Cryptographically secure from 8 upwards as of this writing
+
+/// A random number generator that uses the ChaCha20 algorithm [1].
+///
+/// The ChaCha algorithm is widely accepted as suitable for
+/// cryptographic purposes, but this implementation has not been
+/// verified as such. Prefer a generator like `OsRng` that defers to
+/// the operating system for cases that need high security.
+///
+/// [1]: D. J. Bernstein, [*ChaCha, a variant of
+/// Salsa20*](http://cr.yp.to/chacha.html)
+#[derive(Copy, Clone, Debug)]
+pub struct ChaChaRng {
+ buffer: [w32; STATE_WORDS], // Internal buffer of output
+ state: [w32; STATE_WORDS], // Initial state
+ index: usize, // Index into state
+}
+
+static EMPTY: ChaChaRng = ChaChaRng {
+ buffer: [w(0); STATE_WORDS],
+ state: [w(0); STATE_WORDS],
+ index: STATE_WORDS
+};
+
+
+macro_rules! quarter_round{
+ ($a: expr, $b: expr, $c: expr, $d: expr) => {{
+ $a = $a + $b; $d = $d ^ $a; $d = w($d.0.rotate_left(16));
+ $c = $c + $d; $b = $b ^ $c; $b = w($b.0.rotate_left(12));
+ $a = $a + $b; $d = $d ^ $a; $d = w($d.0.rotate_left( 8));
+ $c = $c + $d; $b = $b ^ $c; $b = w($b.0.rotate_left( 7));
+ }}
+}
+
+macro_rules! double_round{
+ ($x: expr) => {{
+ // Column round
+ quarter_round!($x[ 0], $x[ 4], $x[ 8], $x[12]);
+ quarter_round!($x[ 1], $x[ 5], $x[ 9], $x[13]);
+ quarter_round!($x[ 2], $x[ 6], $x[10], $x[14]);
+ quarter_round!($x[ 3], $x[ 7], $x[11], $x[15]);
+ // Diagonal round
+ quarter_round!($x[ 0], $x[ 5], $x[10], $x[15]);
+ quarter_round!($x[ 1], $x[ 6], $x[11], $x[12]);
+ quarter_round!($x[ 2], $x[ 7], $x[ 8], $x[13]);
+ quarter_round!($x[ 3], $x[ 4], $x[ 9], $x[14]);
+ }}
+}
+
+#[inline]
+fn core(output: &mut [w32; STATE_WORDS], input: &[w32; STATE_WORDS]) {
+ *output = *input;
+
+ for _ in 0..CHACHA_ROUNDS / 2 {
+ double_round!(output);
+ }
+
+ for i in 0..STATE_WORDS {
+ output[i] = output[i] + input[i];
+ }
+}
+
+impl ChaChaRng {
+
+ /// Create an ChaCha random number generator using the default
+ /// fixed key of 8 zero words.
+ ///
+ /// # Examples
+ ///
+ /// ```rust
+ /// use rand::{Rng, ChaChaRng};
+ ///
+ /// let mut ra = ChaChaRng::new_unseeded();
+ /// println!("{:?}", ra.next_u32());
+ /// println!("{:?}", ra.next_u32());
+ /// ```
+ ///
+ /// Since this equivalent to a RNG with a fixed seed, repeated executions
+ /// of an unseeded RNG will produce the same result. This code sample will
+ /// consistently produce:
+ ///
+ /// - 2917185654
+ /// - 2419978656
+ pub fn new_unseeded() -> ChaChaRng {
+ let mut rng = EMPTY;
+ rng.init(&[0; KEY_WORDS]);
+ rng
+ }
+
+ /// Sets the internal 128-bit ChaCha counter to
+ /// a user-provided value. This permits jumping
+ /// arbitrarily ahead (or backwards) in the pseudorandom stream.
+ ///
+ /// Since the nonce words are used to extend the counter to 128 bits,
+ /// users wishing to obtain the conventional ChaCha pseudorandom stream
+ /// associated with a particular nonce can call this function with
+ /// arguments `0, desired_nonce`.
+ ///
+ /// # Examples
+ ///
+ /// ```rust
+ /// use rand::{Rng, ChaChaRng};
+ ///
+ /// let mut ra = ChaChaRng::new_unseeded();
+ /// ra.set_counter(0u64, 1234567890u64);
+ /// println!("{:?}", ra.next_u32());
+ /// println!("{:?}", ra.next_u32());
+ /// ```
+ pub fn set_counter(&mut self, counter_low: u64, counter_high: u64) {
+ self.state[12] = w((counter_low >> 0) as u32);
+ self.state[13] = w((counter_low >> 32) as u32);
+ self.state[14] = w((counter_high >> 0) as u32);
+ self.state[15] = w((counter_high >> 32) as u32);
+ self.index = STATE_WORDS; // force recomputation
+ }
+
+ /// Initializes `self.state` with the appropriate key and constants
+ ///
+ /// We deviate slightly from the ChaCha specification regarding
+ /// the nonce, which is used to extend the counter to 128 bits.
+ /// This is provably as strong as the original cipher, though,
+ /// since any distinguishing attack on our variant also works
+ /// against ChaCha with a chosen-nonce. See the XSalsa20 [1]
+ /// security proof for a more involved example of this.
+ ///
+ /// The modified word layout is:
+ /// ```text
+ /// constant constant constant constant
+ /// key key key key
+ /// key key key key
+ /// counter counter counter counter
+ /// ```
+ /// [1]: Daniel J. Bernstein. [*Extending the Salsa20
+ /// nonce.*](http://cr.yp.to/papers.html#xsalsa)
+ fn init(&mut self, key: &[u32; KEY_WORDS]) {
+ self.state[0] = w(0x61707865);
+ self.state[1] = w(0x3320646E);
+ self.state[2] = w(0x79622D32);
+ self.state[3] = w(0x6B206574);
+
+ for i in 0..KEY_WORDS {
+ self.state[4+i] = w(key[i]);
+ }
+
+ self.state[12] = w(0);
+ self.state[13] = w(0);
+ self.state[14] = w(0);
+ self.state[15] = w(0);
+
+ self.index = STATE_WORDS;
+ }
+
+ /// Refill the internal output buffer (`self.buffer`)
+ fn update(&mut self) {
+ core(&mut self.buffer, &self.state);
+ self.index = 0;
+ // update 128-bit counter
+ self.state[12] = self.state[12] + w(1);
+ if self.state[12] != w(0) { return };
+ self.state[13] = self.state[13] + w(1);
+ if self.state[13] != w(0) { return };
+ self.state[14] = self.state[14] + w(1);
+ if self.state[14] != w(0) { return };
+ self.state[15] = self.state[15] + w(1);
+ }
+}
+
+impl Rng for ChaChaRng {
+ #[inline]
+ fn next_u32(&mut self) -> u32 {
+ if self.index == STATE_WORDS {
+ self.update();
+ }
+
+ let value = self.buffer[self.index % STATE_WORDS];
+ self.index += 1;
+ value.0
+ }
+}
+
+impl<'a> SeedableRng<&'a [u32]> for ChaChaRng {
+
+ fn reseed(&mut self, seed: &'a [u32]) {
+ // reset state
+ self.init(&[0u32; KEY_WORDS]);
+ // set key in place
+ let key = &mut self.state[4 .. 4+KEY_WORDS];
+ for (k, s) in key.iter_mut().zip(seed.iter()) {
+ *k = w(*s);
+ }
+ }
+
+ /// Create a ChaCha generator from a seed,
+ /// obtained from a variable-length u32 array.
+ /// Only up to 8 words are used; if less than 8
+ /// words are used, the remaining are set to zero.
+ fn from_seed(seed: &'a [u32]) -> ChaChaRng {
+ let mut rng = EMPTY;
+ rng.reseed(seed);
+ rng
+ }
+}
+
+impl Rand for ChaChaRng {
+ fn rand<R: Rng>(other: &mut R) -> ChaChaRng {
+ let mut key : [u32; KEY_WORDS] = [0; KEY_WORDS];
+ for word in key.iter_mut() {
+ *word = other.gen();
+ }
+ SeedableRng::from_seed(&key[..])
+ }
+}
+
+
+#[cfg(test)]
+mod test {
+ use {Rng, SeedableRng};
+ use super::ChaChaRng;
+
+ #[test]
+ fn test_rng_rand_seeded() {
+ let s = ::test::rng().gen_iter::<u32>().take(8).collect::<Vec<u32>>();
+ let mut ra: ChaChaRng = SeedableRng::from_seed(&s[..]);
+ let mut rb: ChaChaRng = SeedableRng::from_seed(&s[..]);
+ assert!(::test::iter_eq(ra.gen_ascii_chars().take(100),
+ rb.gen_ascii_chars().take(100)));
+ }
+
+ #[test]
+ fn test_rng_seeded() {
+ let seed : &[_] = &[0,1,2,3,4,5,6,7];
+ let mut ra: ChaChaRng = SeedableRng::from_seed(seed);
+ let mut rb: ChaChaRng = SeedableRng::from_seed(seed);
+ assert!(::test::iter_eq(ra.gen_ascii_chars().take(100),
+ rb.gen_ascii_chars().take(100)));
+ }
+
+ #[test]
+ fn test_rng_reseed() {
+ let s = ::test::rng().gen_iter::<u32>().take(8).collect::<Vec<u32>>();
+ let mut r: ChaChaRng = SeedableRng::from_seed(&s[..]);
+ let string1: String = r.gen_ascii_chars().take(100).collect();
+
+ r.reseed(&s);
+
+ let string2: String = r.gen_ascii_chars().take(100).collect();
+ assert_eq!(string1, string2);
+ }
+
+ #[test]
+ fn test_rng_true_values() {
+ // Test vectors 1 and 2 from
+ // http://tools.ietf.org/html/draft-nir-cfrg-chacha20-poly1305-04
+ let seed : &[_] = &[0u32; 8];
+ let mut ra: ChaChaRng = SeedableRng::from_seed(seed);
+
+ let v = (0..16).map(|_| ra.next_u32()).collect::<Vec<_>>();
+ assert_eq!(v,
+ vec!(0xade0b876, 0x903df1a0, 0xe56a5d40, 0x28bd8653,
+ 0xb819d2bd, 0x1aed8da0, 0xccef36a8, 0xc70d778b,
+ 0x7c5941da, 0x8d485751, 0x3fe02477, 0x374ad8b8,
+ 0xf4b8436a, 0x1ca11815, 0x69b687c3, 0x8665eeb2));
+
+ let v = (0..16).map(|_| ra.next_u32()).collect::<Vec<_>>();
+ assert_eq!(v,
+ vec!(0xbee7079f, 0x7a385155, 0x7c97ba98, 0x0d082d73,
+ 0xa0290fcb, 0x6965e348, 0x3e53c612, 0xed7aee32,
+ 0x7621b729, 0x434ee69c, 0xb03371d5, 0xd539d874,
+ 0x281fed31, 0x45fb0a51, 0x1f0ae1ac, 0x6f4d794b));
+
+
+ let seed : &[_] = &[0,1,2,3,4,5,6,7];
+ let mut ra: ChaChaRng = SeedableRng::from_seed(seed);
+
+ // Store the 17*i-th 32-bit word,
+ // i.e., the i-th word of the i-th 16-word block
+ let mut v : Vec<u32> = Vec::new();
+ for _ in 0..16 {
+ v.push(ra.next_u32());
+ for _ in 0..16 {
+ ra.next_u32();
+ }
+ }
+
+ assert_eq!(v,
+ vec!(0xf225c81a, 0x6ab1be57, 0x04d42951, 0x70858036,
+ 0x49884684, 0x64efec72, 0x4be2d186, 0x3615b384,
+ 0x11cfa18e, 0xd3c50049, 0x75c775f6, 0x434c6530,
+ 0x2c5bad8f, 0x898881dc, 0x5f1c86d9, 0xc1f8e7f4));
+ }
+
+ #[test]
+ fn test_rng_clone() {
+ let seed : &[_] = &[0u32; 8];
+ let mut rng: ChaChaRng = SeedableRng::from_seed(seed);
+ let mut clone = rng.clone();
+ for _ in 0..16 {
+ assert_eq!(rng.next_u64(), clone.next_u64());
+ }
+ }
+}
diff --git a/rand/src/prng/isaac.rs b/rand/src/prng/isaac.rs
new file mode 100644
index 0000000..cf5eb67
--- /dev/null
+++ b/rand/src/prng/isaac.rs
@@ -0,0 +1,328 @@
+// 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 ISAAC random number generator.
+
+#![allow(non_camel_case_types)]
+
+use core::slice;
+use core::iter::repeat;
+use core::num::Wrapping as w;
+use core::fmt;
+
+use {Rng, SeedableRng, Rand};
+
+#[allow(bad_style)]
+type w32 = w<u32>;
+
+const RAND_SIZE_LEN: usize = 8;
+const RAND_SIZE: u32 = 1 << RAND_SIZE_LEN;
+const RAND_SIZE_USIZE: usize = 1 << RAND_SIZE_LEN;
+
+/// A random number generator that uses the ISAAC algorithm[1].
+///
+/// The ISAAC algorithm is generally accepted as suitable for
+/// cryptographic purposes, but this implementation has not be
+/// verified as such. Prefer a generator like `OsRng` that defers to
+/// the operating system for cases that need high security.
+///
+/// [1]: Bob Jenkins, [*ISAAC: A fast cryptographic random number
+/// generator*](http://www.burtleburtle.net/bob/rand/isaacafa.html)
+#[derive(Copy)]
+pub struct IsaacRng {
+ cnt: u32,
+ rsl: [w32; RAND_SIZE_USIZE],
+ mem: [w32; RAND_SIZE_USIZE],
+ a: w32,
+ b: w32,
+ c: w32,
+}
+
+static EMPTY: IsaacRng = IsaacRng {
+ cnt: 0,
+ rsl: [w(0); RAND_SIZE_USIZE],
+ mem: [w(0); RAND_SIZE_USIZE],
+ a: w(0), b: w(0), c: w(0),
+};
+
+impl IsaacRng {
+
+ /// Create an ISAAC random number generator using the default
+ /// fixed seed.
+ pub fn new_unseeded() -> IsaacRng {
+ let mut rng = EMPTY;
+ rng.init(false);
+ rng
+ }
+
+ /// Initialises `self`. If `use_rsl` is true, then use the current value
+ /// of `rsl` as a seed, otherwise construct one algorithmically (not
+ /// randomly).
+ fn init(&mut self, use_rsl: bool) {
+ let mut a = w(0x9e3779b9);
+ let mut b = a;
+ let mut c = a;
+ let mut d = a;
+ let mut e = a;
+ let mut f = a;
+ let mut g = a;
+ let mut h = a;
+
+ macro_rules! mix {
+ () => {{
+ a=a^(b<<11); d=d+a; b=b+c;
+ b=b^(c>>2); e=e+b; c=c+d;
+ c=c^(d<<8); f=f+c; d=d+e;
+ d=d^(e>>16); g=g+d; e=e+f;
+ e=e^(f<<10); h=h+e; f=f+g;
+ f=f^(g>>4); a=a+f; g=g+h;
+ g=g^(h<<8); b=b+g; h=h+a;
+ h=h^(a>>9); c=c+h; a=a+b;
+ }}
+ }
+
+ for _ in 0..4 {
+ mix!();
+ }
+
+ if use_rsl {
+ macro_rules! memloop {
+ ($arr:expr) => {{
+ for i in (0..RAND_SIZE_USIZE/8).map(|i| i * 8) {
+ a=a+$arr[i ]; b=b+$arr[i+1];
+ c=c+$arr[i+2]; d=d+$arr[i+3];
+ e=e+$arr[i+4]; f=f+$arr[i+5];
+ g=g+$arr[i+6]; h=h+$arr[i+7];
+ mix!();
+ self.mem[i ]=a; self.mem[i+1]=b;
+ self.mem[i+2]=c; self.mem[i+3]=d;
+ self.mem[i+4]=e; self.mem[i+5]=f;
+ self.mem[i+6]=g; self.mem[i+7]=h;
+ }
+ }}
+ }
+
+ memloop!(self.rsl);
+ memloop!(self.mem);
+ } else {
+ for i in (0..RAND_SIZE_USIZE/8).map(|i| i * 8) {
+ mix!();
+ self.mem[i ]=a; self.mem[i+1]=b;
+ self.mem[i+2]=c; self.mem[i+3]=d;
+ self.mem[i+4]=e; self.mem[i+5]=f;
+ self.mem[i+6]=g; self.mem[i+7]=h;
+ }
+ }
+
+ self.isaac();
+ }
+
+ /// Refills the output buffer (`self.rsl`)
+ #[inline]
+ fn isaac(&mut self) {
+ self.c = self.c + w(1);
+ // abbreviations
+ let mut a = self.a;
+ let mut b = self.b + self.c;
+
+ const MIDPOINT: usize = RAND_SIZE_USIZE / 2;
+
+ macro_rules! ind {
+ ($x:expr) => ( self.mem[($x >> 2usize).0 as usize & (RAND_SIZE_USIZE - 1)] )
+ }
+
+ let r = [(0, MIDPOINT), (MIDPOINT, 0)];
+ for &(mr_offset, m2_offset) in r.iter() {
+
+ macro_rules! rngstepp {
+ ($j:expr, $shift:expr) => {{
+ let base = $j;
+ let mix = a << $shift;
+
+ let x = self.mem[base + mr_offset];
+ a = (a ^ mix) + self.mem[base + m2_offset];
+ let y = ind!(x) + a + b;
+ self.mem[base + mr_offset] = y;
+
+ b = ind!(y >> RAND_SIZE_LEN) + x;
+ self.rsl[base + mr_offset] = b;
+ }}
+ }
+
+ macro_rules! rngstepn {
+ ($j:expr, $shift:expr) => {{
+ let base = $j;
+ let mix = a >> $shift;
+
+ let x = self.mem[base + mr_offset];
+ a = (a ^ mix) + self.mem[base + m2_offset];
+ let y = ind!(x) + a + b;
+ self.mem[base + mr_offset] = y;
+
+ b = ind!(y >> RAND_SIZE_LEN) + x;
+ self.rsl[base + mr_offset] = b;
+ }}
+ }
+
+ for i in (0..MIDPOINT/4).map(|i| i * 4) {
+ rngstepp!(i + 0, 13);
+ rngstepn!(i + 1, 6);
+ rngstepp!(i + 2, 2);
+ rngstepn!(i + 3, 16);
+ }
+ }
+
+ self.a = a;
+ self.b = b;
+ self.cnt = RAND_SIZE;
+ }
+}
+
+// Cannot be derived because [u32; 256] does not implement Clone
+impl Clone for IsaacRng {
+ fn clone(&self) -> IsaacRng {
+ *self
+ }
+}
+
+impl Rng for IsaacRng {
+ #[inline]
+ fn next_u32(&mut self) -> u32 {
+ if self.cnt == 0 {
+ // make some more numbers
+ self.isaac();
+ }
+ self.cnt -= 1;
+
+ // self.cnt is at most RAND_SIZE, but that is before the
+ // subtraction above. We want to index without bounds
+ // checking, but this could lead to incorrect code if someone
+ // misrefactors, so we check, sometimes.
+ //
+ // (Changes here should be reflected in Isaac64Rng.next_u64.)
+ debug_assert!(self.cnt < RAND_SIZE);
+
+ // (the % is cheaply telling the optimiser that we're always
+ // in bounds, without unsafe. NB. this is a power of two, so
+ // it optimises to a bitwise mask).
+ self.rsl[(self.cnt % RAND_SIZE) as usize].0
+ }
+}
+
+impl<'a> SeedableRng<&'a [u32]> for IsaacRng {
+ fn reseed(&mut self, seed: &'a [u32]) {
+ // make the seed into [seed[0], seed[1], ..., seed[seed.len()
+ // - 1], 0, 0, ...], to fill rng.rsl.
+ let seed_iter = seed.iter().map(|&x| x).chain(repeat(0u32));
+
+ for (rsl_elem, seed_elem) in self.rsl.iter_mut().zip(seed_iter) {
+ *rsl_elem = w(seed_elem);
+ }
+ self.cnt = 0;
+ self.a = w(0);
+ self.b = w(0);
+ self.c = w(0);
+
+ self.init(true);
+ }
+
+ /// Create an ISAAC random number generator with a seed. This can
+ /// be any length, although the maximum number of elements used is
+ /// 256 and any more will be silently ignored. A generator
+ /// constructed with a given seed will generate the same sequence
+ /// of values as all other generators constructed with that seed.
+ fn from_seed(seed: &'a [u32]) -> IsaacRng {
+ let mut rng = EMPTY;
+ rng.reseed(seed);
+ rng
+ }
+}
+
+impl Rand for IsaacRng {
+ fn rand<R: Rng>(other: &mut R) -> IsaacRng {
+ let mut ret = EMPTY;
+ unsafe {
+ let ptr = ret.rsl.as_mut_ptr() as *mut u8;
+
+ let slice = slice::from_raw_parts_mut(ptr, RAND_SIZE_USIZE * 4);
+ other.fill_bytes(slice);
+ }
+ ret.cnt = 0;
+ ret.a = w(0);
+ ret.b = w(0);
+ ret.c = w(0);
+
+ ret.init(true);
+ return ret;
+ }
+}
+
+impl fmt::Debug for IsaacRng {
+ fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+ write!(f, "IsaacRng {{}}")
+ }
+}
+
+#[cfg(test)]
+mod test {
+ use {Rng, SeedableRng};
+ use super::IsaacRng;
+
+ #[test]
+ fn test_rng_32_rand_seeded() {
+ let s = ::test::rng().gen_iter::<u32>().take(256).collect::<Vec<u32>>();
+ let mut ra: IsaacRng = SeedableRng::from_seed(&s[..]);
+ let mut rb: IsaacRng = SeedableRng::from_seed(&s[..]);
+ assert!(::test::iter_eq(ra.gen_ascii_chars().take(100),
+ rb.gen_ascii_chars().take(100)));
+ }
+
+ #[test]
+ fn test_rng_32_seeded() {
+ let seed: &[_] = &[1, 23, 456, 7890, 12345];
+ let mut ra: IsaacRng = SeedableRng::from_seed(seed);
+ let mut rb: IsaacRng = SeedableRng::from_seed(seed);
+ assert!(::test::iter_eq(ra.gen_ascii_chars().take(100),
+ rb.gen_ascii_chars().take(100)));
+ }
+
+ #[test]
+ fn test_rng_32_reseed() {
+ let s = ::test::rng().gen_iter::<u32>().take(256).collect::<Vec<u32>>();
+ let mut r: IsaacRng = SeedableRng::from_seed(&s[..]);
+ let string1: String = r.gen_ascii_chars().take(100).collect();
+
+ r.reseed(&s[..]);
+
+ let string2: String = r.gen_ascii_chars().take(100).collect();
+ assert_eq!(string1, string2);
+ }
+
+ #[test]
+ fn test_rng_32_true_values() {
+ let seed: &[_] = &[1, 23, 456, 7890, 12345];
+ let mut ra: IsaacRng = SeedableRng::from_seed(seed);
+ // Regression test that isaac is actually using the above vector
+ let v = (0..10).map(|_| ra.next_u32()).collect::<Vec<_>>();
+ assert_eq!(v,
+ vec!(2558573138, 873787463, 263499565, 2103644246, 3595684709,
+ 4203127393, 264982119, 2765226902, 2737944514, 3900253796));
+
+ let seed: &[_] = &[12345, 67890, 54321, 9876];
+ let mut rb: IsaacRng = SeedableRng::from_seed(seed);
+ // skip forward to the 10000th number
+ for _ in 0..10000 { rb.next_u32(); }
+
+ let v = (0..10).map(|_| rb.next_u32()).collect::<Vec<_>>();
+ assert_eq!(v,
+ vec!(3676831399, 3183332890, 2834741178, 3854698763, 2717568474,
+ 1576568959, 3507990155, 179069555, 141456972, 2478885421));
+ }
+}
diff --git a/rand/src/prng/isaac64.rs b/rand/src/prng/isaac64.rs
new file mode 100644
index 0000000..b98e3fe
--- /dev/null
+++ b/rand/src/prng/isaac64.rs
@@ -0,0 +1,340 @@
+// 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 ISAAC-64 random number generator.
+
+use core::slice;
+use core::iter::repeat;
+use core::num::Wrapping as w;
+use core::fmt;
+
+use {Rng, SeedableRng, Rand};
+
+#[allow(bad_style)]
+type w64 = w<u64>;
+
+const RAND_SIZE_64_LEN: usize = 8;
+const RAND_SIZE_64: usize = 1 << RAND_SIZE_64_LEN;
+
+/// A random number generator that uses ISAAC-64[1], the 64-bit
+/// variant of the ISAAC algorithm.
+///
+/// The ISAAC algorithm is generally accepted as suitable for
+/// cryptographic purposes, but this implementation has not be
+/// verified as such. Prefer a generator like `OsRng` that defers to
+/// the operating system for cases that need high security.
+///
+/// [1]: Bob Jenkins, [*ISAAC: A fast cryptographic random number
+/// generator*](http://www.burtleburtle.net/bob/rand/isaacafa.html)
+#[derive(Copy)]
+pub struct Isaac64Rng {
+ cnt: usize,
+ rsl: [w64; RAND_SIZE_64],
+ mem: [w64; RAND_SIZE_64],
+ a: w64,
+ b: w64,
+ c: w64,
+}
+
+static EMPTY_64: Isaac64Rng = Isaac64Rng {
+ cnt: 0,
+ rsl: [w(0); RAND_SIZE_64],
+ mem: [w(0); RAND_SIZE_64],
+ a: w(0), b: w(0), c: w(0),
+};
+
+impl Isaac64Rng {
+ /// Create a 64-bit ISAAC random number generator using the
+ /// default fixed seed.
+ pub fn new_unseeded() -> Isaac64Rng {
+ let mut rng = EMPTY_64;
+ rng.init(false);
+ rng
+ }
+
+ /// Initialises `self`. If `use_rsl` is true, then use the current value
+ /// of `rsl` as a seed, otherwise construct one algorithmically (not
+ /// randomly).
+ fn init(&mut self, use_rsl: bool) {
+ macro_rules! init {
+ ($var:ident) => (
+ let mut $var = w(0x9e3779b97f4a7c13);
+ )
+ }
+ init!(a); init!(b); init!(c); init!(d);
+ init!(e); init!(f); init!(g); init!(h);
+
+ macro_rules! mix {
+ () => {{
+ a=a-e; f=f^(h>>9); h=h+a;
+ b=b-f; g=g^(a<<9); a=a+b;
+ c=c-g; h=h^(b>>23); b=b+c;
+ d=d-h; a=a^(c<<15); c=c+d;
+ e=e-a; b=b^(d>>14); d=d+e;
+ f=f-b; c=c^(e<<20); e=e+f;
+ g=g-c; d=d^(f>>17); f=f+g;
+ h=h-d; e=e^(g<<14); g=g+h;
+ }}
+ }
+
+ for _ in 0..4 {
+ mix!();
+ }
+
+ if use_rsl {
+ macro_rules! memloop {
+ ($arr:expr) => {{
+ for i in (0..RAND_SIZE_64 / 8).map(|i| i * 8) {
+ a=a+$arr[i ]; b=b+$arr[i+1];
+ c=c+$arr[i+2]; d=d+$arr[i+3];
+ e=e+$arr[i+4]; f=f+$arr[i+5];
+ g=g+$arr[i+6]; h=h+$arr[i+7];
+ mix!();
+ self.mem[i ]=a; self.mem[i+1]=b;
+ self.mem[i+2]=c; self.mem[i+3]=d;
+ self.mem[i+4]=e; self.mem[i+5]=f;
+ self.mem[i+6]=g; self.mem[i+7]=h;
+ }
+ }}
+ }
+
+ memloop!(self.rsl);
+ memloop!(self.mem);
+ } else {
+ for i in (0..RAND_SIZE_64 / 8).map(|i| i * 8) {
+ mix!();
+ self.mem[i ]=a; self.mem[i+1]=b;
+ self.mem[i+2]=c; self.mem[i+3]=d;
+ self.mem[i+4]=e; self.mem[i+5]=f;
+ self.mem[i+6]=g; self.mem[i+7]=h;
+ }
+ }
+
+ self.isaac64();
+ }
+
+ /// Refills the output buffer (`self.rsl`)
+ fn isaac64(&mut self) {
+ self.c = self.c + w(1);
+ // abbreviations
+ let mut a = self.a;
+ let mut b = self.b + self.c;
+ const MIDPOINT: usize = RAND_SIZE_64 / 2;
+ const MP_VEC: [(usize, usize); 2] = [(0,MIDPOINT), (MIDPOINT, 0)];
+ macro_rules! ind {
+ ($x:expr) => {
+ *self.mem.get_unchecked((($x >> 3usize).0 as usize) & (RAND_SIZE_64 - 1))
+ }
+ }
+
+ for &(mr_offset, m2_offset) in MP_VEC.iter() {
+ for base in (0..MIDPOINT / 4).map(|i| i * 4) {
+
+ macro_rules! rngstepp {
+ ($j:expr, $shift:expr) => {{
+ let base = base + $j;
+ let mix = a ^ (a << $shift);
+ let mix = if $j == 0 {!mix} else {mix};
+
+ unsafe {
+ let x = *self.mem.get_unchecked(base + mr_offset);
+ a = mix + *self.mem.get_unchecked(base + m2_offset);
+ let y = ind!(x) + a + b;
+ *self.mem.get_unchecked_mut(base + mr_offset) = y;
+
+ b = ind!(y >> RAND_SIZE_64_LEN) + x;
+ *self.rsl.get_unchecked_mut(base + mr_offset) = b;
+ }
+ }}
+ }
+
+ macro_rules! rngstepn {
+ ($j:expr, $shift:expr) => {{
+ let base = base + $j;
+ let mix = a ^ (a >> $shift);
+ let mix = if $j == 0 {!mix} else {mix};
+
+ unsafe {
+ let x = *self.mem.get_unchecked(base + mr_offset);
+ a = mix + *self.mem.get_unchecked(base + m2_offset);
+ let y = ind!(x) + a + b;
+ *self.mem.get_unchecked_mut(base + mr_offset) = y;
+
+ b = ind!(y >> RAND_SIZE_64_LEN) + x;
+ *self.rsl.get_unchecked_mut(base + mr_offset) = b;
+ }
+ }}
+ }
+
+ rngstepp!(0, 21);
+ rngstepn!(1, 5);
+ rngstepp!(2, 12);
+ rngstepn!(3, 33);
+ }
+ }
+
+ self.a = a;
+ self.b = b;
+ self.cnt = RAND_SIZE_64;
+ }
+}
+
+// Cannot be derived because [u32; 256] does not implement Clone
+impl Clone for Isaac64Rng {
+ fn clone(&self) -> Isaac64Rng {
+ *self
+ }
+}
+
+impl Rng for Isaac64Rng {
+ #[inline]
+ fn next_u32(&mut self) -> u32 {
+ self.next_u64() as u32
+ }
+
+ #[inline]
+ fn next_u64(&mut self) -> u64 {
+ if self.cnt == 0 {
+ // make some more numbers
+ self.isaac64();
+ }
+ self.cnt -= 1;
+
+ // See corresponding location in IsaacRng.next_u32 for
+ // explanation.
+ debug_assert!(self.cnt < RAND_SIZE_64);
+ self.rsl[(self.cnt % RAND_SIZE_64) as usize].0
+ }
+}
+
+impl<'a> SeedableRng<&'a [u64]> for Isaac64Rng {
+ fn reseed(&mut self, seed: &'a [u64]) {
+ // make the seed into [seed[0], seed[1], ..., seed[seed.len()
+ // - 1], 0, 0, ...], to fill rng.rsl.
+ let seed_iter = seed.iter().map(|&x| x).chain(repeat(0u64));
+
+ for (rsl_elem, seed_elem) in self.rsl.iter_mut().zip(seed_iter) {
+ *rsl_elem = w(seed_elem);
+ }
+ self.cnt = 0;
+ self.a = w(0);
+ self.b = w(0);
+ self.c = w(0);
+
+ self.init(true);
+ }
+
+ /// Create an ISAAC random number generator with a seed. This can
+ /// be any length, although the maximum number of elements used is
+ /// 256 and any more will be silently ignored. A generator
+ /// constructed with a given seed will generate the same sequence
+ /// of values as all other generators constructed with that seed.
+ fn from_seed(seed: &'a [u64]) -> Isaac64Rng {
+ let mut rng = EMPTY_64;
+ rng.reseed(seed);
+ rng
+ }
+}
+
+impl Rand for Isaac64Rng {
+ fn rand<R: Rng>(other: &mut R) -> Isaac64Rng {
+ let mut ret = EMPTY_64;
+ unsafe {
+ let ptr = ret.rsl.as_mut_ptr() as *mut u8;
+
+ let slice = slice::from_raw_parts_mut(ptr, RAND_SIZE_64 * 8);
+ other.fill_bytes(slice);
+ }
+ ret.cnt = 0;
+ ret.a = w(0);
+ ret.b = w(0);
+ ret.c = w(0);
+
+ ret.init(true);
+ return ret;
+ }
+}
+
+impl fmt::Debug for Isaac64Rng {
+ fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+ write!(f, "Isaac64Rng {{}}")
+ }
+}
+
+#[cfg(test)]
+mod test {
+ use {Rng, SeedableRng};
+ use super::Isaac64Rng;
+
+ #[test]
+ fn test_rng_64_rand_seeded() {
+ let s = ::test::rng().gen_iter::<u64>().take(256).collect::<Vec<u64>>();
+ let mut ra: Isaac64Rng = SeedableRng::from_seed(&s[..]);
+ let mut rb: Isaac64Rng = SeedableRng::from_seed(&s[..]);
+ assert!(::test::iter_eq(ra.gen_ascii_chars().take(100),
+ rb.gen_ascii_chars().take(100)));
+ }
+
+ #[test]
+ fn test_rng_64_seeded() {
+ let seed: &[_] = &[1, 23, 456, 7890, 12345];
+ let mut ra: Isaac64Rng = SeedableRng::from_seed(seed);
+ let mut rb: Isaac64Rng = SeedableRng::from_seed(seed);
+ assert!(::test::iter_eq(ra.gen_ascii_chars().take(100),
+ rb.gen_ascii_chars().take(100)));
+ }
+
+ #[test]
+ fn test_rng_64_reseed() {
+ let s = ::test::rng().gen_iter::<u64>().take(256).collect::<Vec<u64>>();
+ let mut r: Isaac64Rng = SeedableRng::from_seed(&s[..]);
+ let string1: String = r.gen_ascii_chars().take(100).collect();
+
+ r.reseed(&s[..]);
+
+ let string2: String = r.gen_ascii_chars().take(100).collect();
+ assert_eq!(string1, string2);
+ }
+
+ #[test]
+ fn test_rng_64_true_values() {
+ let seed: &[_] = &[1, 23, 456, 7890, 12345];
+ let mut ra: Isaac64Rng = SeedableRng::from_seed(seed);
+ // Regression test that isaac is actually using the above vector
+ let v = (0..10).map(|_| ra.next_u64()).collect::<Vec<_>>();
+ assert_eq!(v,
+ vec!(547121783600835980, 14377643087320773276, 17351601304698403469,
+ 1238879483818134882, 11952566807690396487, 13970131091560099343,
+ 4469761996653280935, 15552757044682284409, 6860251611068737823,
+ 13722198873481261842));
+
+ let seed: &[_] = &[12345, 67890, 54321, 9876];
+ let mut rb: Isaac64Rng = SeedableRng::from_seed(seed);
+ // skip forward to the 10000th number
+ for _ in 0..10000 { rb.next_u64(); }
+
+ let v = (0..10).map(|_| rb.next_u64()).collect::<Vec<_>>();
+ assert_eq!(v,
+ vec!(18143823860592706164, 8491801882678285927, 2699425367717515619,
+ 17196852593171130876, 2606123525235546165, 15790932315217671084,
+ 596345674630742204, 9947027391921273664, 11788097613744130851,
+ 10391409374914919106));
+ }
+
+ #[test]
+ fn test_rng_clone() {
+ let seed: &[_] = &[1, 23, 456, 7890, 12345];
+ let mut rng: Isaac64Rng = SeedableRng::from_seed(seed);
+ let mut clone = rng.clone();
+ for _ in 0..16 {
+ assert_eq!(rng.next_u64(), clone.next_u64());
+ }
+ }
+}
diff --git a/rand/src/prng/mod.rs b/rand/src/prng/mod.rs
new file mode 100644
index 0000000..ed3e018
--- /dev/null
+++ b/rand/src/prng/mod.rs
@@ -0,0 +1,51 @@
+// Copyright 2017 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.
+
+//! Pseudo random number generators are algorithms to produce *apparently
+//! random* numbers deterministically, and usually fairly quickly.
+//!
+//! So long as the algorithm is computationally secure, is initialised with
+//! sufficient entropy (i.e. unknown by an attacker), and its internal state is
+//! also protected (unknown to an attacker), the output will also be
+//! *computationally secure*. Computationally Secure Pseudo Random Number
+//! Generators (CSPRNGs) are thus suitable sources of random numbers for
+//! cryptography. There are a couple of gotchas here, however. First, the seed
+//! used for initialisation must be unknown. Usually this should be provided by
+//! the operating system and should usually be secure, however this may not
+//! always be the case (especially soon after startup). Second, user-space
+//! memory may be vulnerable, for example when written to swap space, and after
+//! forking a child process should reinitialise any user-space PRNGs. For this
+//! reason it may be preferable to source random numbers directly from the OS
+//! for cryptographic applications.
+//!
+//! PRNGs are also widely used for non-cryptographic uses: randomised
+//! algorithms, simulations, games. In these applications it is usually not
+//! important for numbers to be cryptographically *unguessable*, but even
+//! distribution and independence from other samples (from the point of view
+//! of someone unaware of the algorithm used, at least) may still be important.
+//! Good PRNGs should satisfy these properties, but do not take them for
+//! granted; Wikipedia's article on
+//! [Pseudorandom number generators](https://en.wikipedia.org/wiki/Pseudorandom_number_generator)
+//! provides some background on this topic.
+//!
+//! Care should be taken when seeding (initialising) PRNGs. Some PRNGs have
+//! short periods for some seeds. If one PRNG is seeded from another using the
+//! same algorithm, it is possible that both will yield the same sequence of
+//! values (with some lag).
+
+mod chacha;
+mod isaac;
+mod isaac64;
+mod xorshift;
+
+pub use self::chacha::ChaChaRng;
+pub use self::isaac::IsaacRng;
+pub use self::isaac64::Isaac64Rng;
+pub use self::xorshift::XorShiftRng;
diff --git a/rand/src/prng/xorshift.rs b/rand/src/prng/xorshift.rs
new file mode 100644
index 0000000..dd367e9
--- /dev/null
+++ b/rand/src/prng/xorshift.rs
@@ -0,0 +1,101 @@
+// Copyright 2017 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.
+
+//! Xorshift generators
+
+use core::num::Wrapping as w;
+use {Rng, SeedableRng, Rand};
+
+/// An Xorshift[1] random number
+/// generator.
+///
+/// The Xorshift algorithm is not suitable for cryptographic purposes
+/// but is very fast. If you do not know for sure that it fits your
+/// requirements, use a more secure one such as `IsaacRng` or `OsRng`.
+///
+/// [1]: Marsaglia, George (July 2003). ["Xorshift
+/// RNGs"](http://www.jstatsoft.org/v08/i14/paper). *Journal of
+/// Statistical Software*. Vol. 8 (Issue 14).
+#[allow(missing_copy_implementations)]
+#[derive(Clone, Debug)]
+pub struct XorShiftRng {
+ x: w<u32>,
+ y: w<u32>,
+ z: w<u32>,
+ w: w<u32>,
+}
+
+impl XorShiftRng {
+ /// Creates a new XorShiftRng instance which is not seeded.
+ ///
+ /// The initial values of this RNG are constants, so all generators created
+ /// by this function will yield the same stream of random numbers. It is
+ /// highly recommended that this is created through `SeedableRng` instead of
+ /// this function
+ pub fn new_unseeded() -> XorShiftRng {
+ XorShiftRng {
+ x: w(0x193a6754),
+ y: w(0xa8a7d469),
+ z: w(0x97830e05),
+ w: w(0x113ba7bb),
+ }
+ }
+}
+
+impl Rng for XorShiftRng {
+ #[inline]
+ fn next_u32(&mut self) -> u32 {
+ let x = self.x;
+ let t = x ^ (x << 11);
+ self.x = self.y;
+ self.y = self.z;
+ self.z = self.w;
+ let w_ = self.w;
+ self.w = w_ ^ (w_ >> 19) ^ (t ^ (t >> 8));
+ self.w.0
+ }
+}
+
+impl SeedableRng<[u32; 4]> for XorShiftRng {
+ /// Reseed an XorShiftRng. This will panic if `seed` is entirely 0.
+ fn reseed(&mut self, seed: [u32; 4]) {
+ assert!(!seed.iter().all(|&x| x == 0),
+ "XorShiftRng.reseed called with an all zero seed.");
+
+ self.x = w(seed[0]);
+ self.y = w(seed[1]);
+ self.z = w(seed[2]);
+ self.w = w(seed[3]);
+ }
+
+ /// Create a new XorShiftRng. This will panic if `seed` is entirely 0.
+ fn from_seed(seed: [u32; 4]) -> XorShiftRng {
+ assert!(!seed.iter().all(|&x| x == 0),
+ "XorShiftRng::from_seed called with an all zero seed.");
+
+ XorShiftRng {
+ x: w(seed[0]),
+ y: w(seed[1]),
+ z: w(seed[2]),
+ w: w(seed[3]),
+ }
+ }
+}
+
+impl Rand for XorShiftRng {
+ fn rand<R: Rng>(rng: &mut R) -> XorShiftRng {
+ let mut tuple: (u32, u32, u32, u32) = rng.gen();
+ while tuple == (0, 0, 0, 0) {
+ tuple = rng.gen();
+ }
+ let (x, y, z, w_) = tuple;
+ XorShiftRng { x: w(x), y: w(y), z: w(z), w: w(w_) }
+ }
+}
diff --git a/rand/src/rand_impls.rs b/rand/src/rand_impls.rs
new file mode 100644
index 0000000..a865bb6
--- /dev/null
+++ b/rand/src/rand_impls.rs
@@ -0,0 +1,299 @@
+// Copyright 2013-2014 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 implementations of `Rand` for the built-in types.
+
+use core::{char, mem};
+
+use {Rand,Rng};
+
+impl Rand for isize {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> isize {
+ if mem::size_of::<isize>() == 4 {
+ rng.gen::<i32>() as isize
+ } else {
+ rng.gen::<i64>() as isize
+ }
+ }
+}
+
+impl Rand for i8 {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> i8 {
+ rng.next_u32() as i8
+ }
+}
+
+impl Rand for i16 {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> i16 {
+ rng.next_u32() as i16
+ }
+}
+
+impl Rand for i32 {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> i32 {
+ rng.next_u32() as i32
+ }
+}
+
+impl Rand for i64 {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> i64 {
+ rng.next_u64() as i64
+ }
+}
+
+#[cfg(feature = "i128_support")]
+impl Rand for i128 {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> i128 {
+ rng.gen::<u128>() as i128
+ }
+}
+
+impl Rand for usize {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> usize {
+ if mem::size_of::<usize>() == 4 {
+ rng.gen::<u32>() as usize
+ } else {
+ rng.gen::<u64>() as usize
+ }
+ }
+}
+
+impl Rand for u8 {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> u8 {
+ rng.next_u32() as u8
+ }
+}
+
+impl Rand for u16 {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> u16 {
+ rng.next_u32() as u16
+ }
+}
+
+impl Rand for u32 {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> u32 {
+ rng.next_u32()
+ }
+}
+
+impl Rand for u64 {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> u64 {
+ rng.next_u64()
+ }
+}
+
+#[cfg(feature = "i128_support")]
+impl Rand for u128 {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> u128 {
+ ((rng.next_u64() as u128) << 64) | (rng.next_u64() as u128)
+ }
+}
+
+
+macro_rules! float_impls {
+ ($mod_name:ident, $ty:ty, $mantissa_bits:expr, $method_name:ident) => {
+ mod $mod_name {
+ use {Rand, Rng, Open01, Closed01};
+
+ const SCALE: $ty = (1u64 << $mantissa_bits) as $ty;
+
+ impl Rand for $ty {
+ /// Generate a floating point number in the half-open
+ /// interval `[0,1)`.
+ ///
+ /// See `Closed01` for the closed interval `[0,1]`,
+ /// and `Open01` for the open interval `(0,1)`.
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> $ty {
+ rng.$method_name()
+ }
+ }
+ impl Rand for Open01<$ty> {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> Open01<$ty> {
+ // add a small amount (specifically 2 bits below
+ // the precision of f64/f32 at 1.0), so that small
+ // numbers are larger than 0, but large numbers
+ // aren't pushed to/above 1.
+ Open01(rng.$method_name() + 0.25 / SCALE)
+ }
+ }
+ impl Rand for Closed01<$ty> {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> Closed01<$ty> {
+ // rescale so that 1.0 - epsilon becomes 1.0
+ // precisely.
+ Closed01(rng.$method_name() * SCALE / (SCALE - 1.0))
+ }
+ }
+ }
+ }
+}
+float_impls! { f64_rand_impls, f64, 53, next_f64 }
+float_impls! { f32_rand_impls, f32, 24, next_f32 }
+
+impl Rand for char {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> char {
+ // a char is 21 bits
+ const CHAR_MASK: u32 = 0x001f_ffff;
+ loop {
+ // Rejection sampling. About 0.2% of numbers with at most
+ // 21-bits are invalid codepoints (surrogates), so this
+ // will succeed first go almost every time.
+ match char::from_u32(rng.next_u32() & CHAR_MASK) {
+ Some(c) => return c,
+ None => {}
+ }
+ }
+ }
+}
+
+impl Rand for bool {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> bool {
+ rng.gen::<u8>() & 1 == 1
+ }
+}
+
+macro_rules! tuple_impl {
+ // use variables to indicate the arity of the tuple
+ ($($tyvar:ident),* ) => {
+ // the trailing commas are for the 1 tuple
+ impl<
+ $( $tyvar : Rand ),*
+ > Rand for ( $( $tyvar ),* , ) {
+
+ #[inline]
+ fn rand<R: Rng>(_rng: &mut R) -> ( $( $tyvar ),* , ) {
+ (
+ // use the $tyvar's to get the appropriate number of
+ // repeats (they're not actually needed)
+ $(
+ _rng.gen::<$tyvar>()
+ ),*
+ ,
+ )
+ }
+ }
+ }
+}
+
+impl Rand for () {
+ #[inline]
+ fn rand<R: Rng>(_: &mut R) -> () { () }
+}
+tuple_impl!{A}
+tuple_impl!{A, B}
+tuple_impl!{A, B, C}
+tuple_impl!{A, B, C, D}
+tuple_impl!{A, B, C, D, E}
+tuple_impl!{A, B, C, D, E, F}
+tuple_impl!{A, B, C, D, E, F, G}
+tuple_impl!{A, B, C, D, E, F, G, H}
+tuple_impl!{A, B, C, D, E, F, G, H, I}
+tuple_impl!{A, B, C, D, E, F, G, H, I, J}
+tuple_impl!{A, B, C, D, E, F, G, H, I, J, K}
+tuple_impl!{A, B, C, D, E, F, G, H, I, J, K, L}
+
+macro_rules! array_impl {
+ {$n:expr, $t:ident, $($ts:ident,)*} => {
+ array_impl!{($n - 1), $($ts,)*}
+
+ impl<T> Rand for [T; $n] where T: Rand {
+ #[inline]
+ fn rand<R: Rng>(_rng: &mut R) -> [T; $n] {
+ [_rng.gen::<$t>(), $(_rng.gen::<$ts>()),*]
+ }
+ }
+ };
+ {$n:expr,} => {
+ impl<T> Rand for [T; $n] {
+ fn rand<R: Rng>(_rng: &mut R) -> [T; $n] { [] }
+ }
+ };
+}
+
+array_impl!{32, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T,}
+
+impl<T:Rand> Rand for Option<T> {
+ #[inline]
+ fn rand<R: Rng>(rng: &mut R) -> Option<T> {
+ if rng.gen() {
+ Some(rng.gen())
+ } else {
+ None
+ }
+ }
+}
+
+#[cfg(test)]
+mod tests {
+ use {Rng, thread_rng, Open01, Closed01};
+
+ struct ConstantRng(u64);
+ impl Rng for ConstantRng {
+ fn next_u32(&mut self) -> u32 {
+ let ConstantRng(v) = *self;
+ v as u32
+ }
+ fn next_u64(&mut self) -> u64 {
+ let ConstantRng(v) = *self;
+ v
+ }
+ }
+
+ #[test]
+ fn floating_point_edge_cases() {
+ // the test for exact equality is correct here.
+ assert!(ConstantRng(0xffff_ffff).gen::<f32>() != 1.0);
+ assert!(ConstantRng(0xffff_ffff_ffff_ffff).gen::<f64>() != 1.0);
+ }
+
+ #[test]
+ fn rand_open() {
+ // this is unlikely to catch an incorrect implementation that
+ // generates exactly 0 or 1, but it keeps it sane.
+ let mut rng = thread_rng();
+ for _ in 0..1_000 {
+ // strict inequalities
+ let Open01(f) = rng.gen::<Open01<f64>>();
+ assert!(0.0 < f && f < 1.0);
+
+ let Open01(f) = rng.gen::<Open01<f32>>();
+ assert!(0.0 < f && f < 1.0);
+ }
+ }
+
+ #[test]
+ fn rand_closed() {
+ let mut rng = thread_rng();
+ for _ in 0..1_000 {
+ // strict inequalities
+ let Closed01(f) = rng.gen::<Closed01<f64>>();
+ assert!(0.0 <= f && f <= 1.0);
+
+ let Closed01(f) = rng.gen::<Closed01<f32>>();
+ assert!(0.0 <= f && f <= 1.0);
+ }
+ }
+}
diff --git a/rand/src/read.rs b/rand/src/read.rs
new file mode 100644
index 0000000..c7351b7
--- /dev/null
+++ b/rand/src/read.rs
@@ -0,0 +1,123 @@
+// 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.
+
+//! A wrapper around any Read to treat it as an RNG.
+
+use std::io::{self, Read};
+use std::mem;
+use Rng;
+
+/// An RNG that reads random bytes straight from a `Read`. This will
+/// work best with an infinite reader, but this is not required.
+///
+/// # Panics
+///
+/// It will panic if it there is insufficient data to fulfill a request.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::{read, Rng};
+///
+/// let data = vec![1, 2, 3, 4, 5, 6, 7, 8];
+/// let mut rng = read::ReadRng::new(&data[..]);
+/// println!("{:x}", rng.gen::<u32>());
+/// ```
+#[derive(Debug)]
+pub struct ReadRng<R> {
+ reader: R
+}
+
+impl<R: Read> ReadRng<R> {
+ /// Create a new `ReadRng` from a `Read`.
+ pub fn new(r: R) -> ReadRng<R> {
+ ReadRng {
+ reader: r
+ }
+ }
+}
+
+impl<R: Read> Rng for ReadRng<R> {
+ fn next_u32(&mut self) -> u32 {
+ // This is designed for speed: reading a LE integer on a LE
+ // platform just involves blitting the bytes into the memory
+ // of the u32, similarly for BE on BE; avoiding byteswapping.
+ let mut buf = [0; 4];
+ fill(&mut self.reader, &mut buf).unwrap();
+ unsafe { *(buf.as_ptr() as *const u32) }
+ }
+ fn next_u64(&mut self) -> u64 {
+ // see above for explanation.
+ let mut buf = [0; 8];
+ fill(&mut self.reader, &mut buf).unwrap();
+ unsafe { *(buf.as_ptr() as *const u64) }
+ }
+ fn fill_bytes(&mut self, v: &mut [u8]) {
+ if v.len() == 0 { return }
+ fill(&mut self.reader, v).unwrap();
+ }
+}
+
+fn fill(r: &mut Read, mut buf: &mut [u8]) -> io::Result<()> {
+ while buf.len() > 0 {
+ match try!(r.read(buf)) {
+ 0 => return Err(io::Error::new(io::ErrorKind::Other,
+ "end of file reached")),
+ n => buf = &mut mem::replace(&mut buf, &mut [])[n..],
+ }
+ }
+ Ok(())
+}
+
+#[cfg(test)]
+mod test {
+ use super::ReadRng;
+ use Rng;
+
+ #[test]
+ fn test_reader_rng_u64() {
+ // transmute from the target to avoid endianness concerns.
+ let v = vec![0u8, 0, 0, 0, 0, 0, 0, 1,
+ 0 , 0, 0, 0, 0, 0, 0, 2,
+ 0, 0, 0, 0, 0, 0, 0, 3];
+ let mut rng = ReadRng::new(&v[..]);
+
+ assert_eq!(rng.next_u64(), 1_u64.to_be());
+ assert_eq!(rng.next_u64(), 2_u64.to_be());
+ assert_eq!(rng.next_u64(), 3_u64.to_be());
+ }
+ #[test]
+ fn test_reader_rng_u32() {
+ let v = vec![0u8, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 3];
+ let mut rng = ReadRng::new(&v[..]);
+
+ assert_eq!(rng.next_u32(), 1_u32.to_be());
+ assert_eq!(rng.next_u32(), 2_u32.to_be());
+ assert_eq!(rng.next_u32(), 3_u32.to_be());
+ }
+ #[test]
+ fn test_reader_rng_fill_bytes() {
+ let v = [1u8, 2, 3, 4, 5, 6, 7, 8];
+ let mut w = [0u8; 8];
+
+ let mut rng = ReadRng::new(&v[..]);
+ rng.fill_bytes(&mut w);
+
+ assert!(v == w);
+ }
+
+ #[test]
+ #[should_panic]
+ fn test_reader_rng_insufficient_bytes() {
+ let mut rng = ReadRng::new(&[][..]);
+ let mut v = [0u8; 3];
+ rng.fill_bytes(&mut v);
+ }
+}
diff --git a/rand/src/reseeding.rs b/rand/src/reseeding.rs
new file mode 100644
index 0000000..1f24e20
--- /dev/null
+++ b/rand/src/reseeding.rs
@@ -0,0 +1,229 @@
+// 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.
+
+//! A wrapper around another RNG that reseeds it after it
+//! generates a certain number of random bytes.
+
+use core::default::Default;
+
+use {Rng, SeedableRng};
+
+/// How many bytes of entropy the underling RNG is allowed to generate
+/// before it is reseeded
+const DEFAULT_GENERATION_THRESHOLD: u64 = 32 * 1024;
+
+/// A wrapper around any RNG which reseeds the underlying RNG after it
+/// has generated a certain number of random bytes.
+#[derive(Debug)]
+pub struct ReseedingRng<R, Rsdr> {
+ rng: R,
+ generation_threshold: u64,
+ bytes_generated: u64,
+ /// Controls the behaviour when reseeding the RNG.
+ pub reseeder: Rsdr,
+}
+
+impl<R: Rng, Rsdr: Reseeder<R>> ReseedingRng<R, Rsdr> {
+ /// Create a new `ReseedingRng` with the given parameters.
+ ///
+ /// # Arguments
+ ///
+ /// * `rng`: the random number generator to use.
+ /// * `generation_threshold`: the number of bytes of entropy at which to reseed the RNG.
+ /// * `reseeder`: the reseeding object to use.
+ pub fn new(rng: R, generation_threshold: u64, reseeder: Rsdr) -> ReseedingRng<R,Rsdr> {
+ ReseedingRng {
+ rng: rng,
+ generation_threshold: generation_threshold,
+ bytes_generated: 0,
+ reseeder: reseeder
+ }
+ }
+
+ /// Reseed the internal RNG if the number of bytes that have been
+ /// generated exceed the threshold.
+ pub fn reseed_if_necessary(&mut self) {
+ if self.bytes_generated >= self.generation_threshold {
+ self.reseeder.reseed(&mut self.rng);
+ self.bytes_generated = 0;
+ }
+ }
+}
+
+
+impl<R: Rng, Rsdr: Reseeder<R>> Rng for ReseedingRng<R, Rsdr> {
+ fn next_u32(&mut self) -> u32 {
+ self.reseed_if_necessary();
+ self.bytes_generated += 4;
+ self.rng.next_u32()
+ }
+
+ fn next_u64(&mut self) -> u64 {
+ self.reseed_if_necessary();
+ self.bytes_generated += 8;
+ self.rng.next_u64()
+ }
+
+ fn fill_bytes(&mut self, dest: &mut [u8]) {
+ self.reseed_if_necessary();
+ self.bytes_generated += dest.len() as u64;
+ self.rng.fill_bytes(dest)
+ }
+}
+
+impl<S, R: SeedableRng<S>, Rsdr: Reseeder<R> + Default>
+ SeedableRng<(Rsdr, S)> for ReseedingRng<R, Rsdr> {
+ fn reseed(&mut self, (rsdr, seed): (Rsdr, S)) {
+ self.rng.reseed(seed);
+ self.reseeder = rsdr;
+ self.bytes_generated = 0;
+ }
+
+ /// Create a new `ReseedingRng` from the given reseeder and
+ /// seed. This uses a default value for `generation_threshold`.
+ fn from_seed((rsdr, seed): (Rsdr, S)) -> ReseedingRng<R, Rsdr> {
+ ReseedingRng {
+ rng: SeedableRng::from_seed(seed),
+ generation_threshold: DEFAULT_GENERATION_THRESHOLD,
+ bytes_generated: 0,
+ reseeder: rsdr
+ }
+ }
+}
+
+/// Something that can be used to reseed an RNG via `ReseedingRng`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::{Rng, SeedableRng, StdRng};
+/// use rand::reseeding::{Reseeder, ReseedingRng};
+///
+/// struct TickTockReseeder { tick: bool }
+/// impl Reseeder<StdRng> for TickTockReseeder {
+/// fn reseed(&mut self, rng: &mut StdRng) {
+/// let val = if self.tick {0} else {1};
+/// rng.reseed(&[val]);
+/// self.tick = !self.tick;
+/// }
+/// }
+/// fn main() {
+/// let rsdr = TickTockReseeder { tick: true };
+///
+/// let inner = StdRng::new().unwrap();
+/// let mut rng = ReseedingRng::new(inner, 10, rsdr);
+///
+/// // this will repeat, because it gets reseeded very regularly.
+/// let s: String = rng.gen_ascii_chars().take(100).collect();
+/// println!("{}", s);
+/// }
+///
+/// ```
+pub trait Reseeder<R> {
+ /// Reseed the given RNG.
+ fn reseed(&mut self, rng: &mut R);
+}
+
+/// Reseed an RNG using a `Default` instance. This reseeds by
+/// replacing the RNG with the result of a `Default::default` call.
+#[derive(Clone, Copy, Debug)]
+pub struct ReseedWithDefault;
+
+impl<R: Rng + Default> Reseeder<R> for ReseedWithDefault {
+ fn reseed(&mut self, rng: &mut R) {
+ *rng = Default::default();
+ }
+}
+impl Default for ReseedWithDefault {
+ fn default() -> ReseedWithDefault { ReseedWithDefault }
+}
+
+#[cfg(test)]
+mod test {
+ use std::default::Default;
+ use std::iter::repeat;
+ use super::{ReseedingRng, ReseedWithDefault};
+ use {SeedableRng, Rng};
+
+ struct Counter {
+ i: u32
+ }
+
+ impl Rng for Counter {
+ fn next_u32(&mut self) -> u32 {
+ self.i += 1;
+ // very random
+ self.i - 1
+ }
+ }
+ impl Default for Counter {
+ fn default() -> Counter {
+ Counter { i: 0 }
+ }
+ }
+ impl SeedableRng<u32> for Counter {
+ fn reseed(&mut self, seed: u32) {
+ self.i = seed;
+ }
+ fn from_seed(seed: u32) -> Counter {
+ Counter { i: seed }
+ }
+ }
+ type MyRng = ReseedingRng<Counter, ReseedWithDefault>;
+
+ #[test]
+ fn test_reseeding() {
+ let mut rs = ReseedingRng::new(Counter {i:0}, 400, ReseedWithDefault);
+
+ let mut i = 0;
+ for _ in 0..1000 {
+ assert_eq!(rs.next_u32(), i % 100);
+ i += 1;
+ }
+ }
+
+ #[test]
+ fn test_rng_seeded() {
+ let mut ra: MyRng = SeedableRng::from_seed((ReseedWithDefault, 2));
+ let mut rb: MyRng = SeedableRng::from_seed((ReseedWithDefault, 2));
+ assert!(::test::iter_eq(ra.gen_ascii_chars().take(100),
+ rb.gen_ascii_chars().take(100)));
+ }
+
+ #[test]
+ fn test_rng_reseed() {
+ let mut r: MyRng = SeedableRng::from_seed((ReseedWithDefault, 3));
+ let string1: String = r.gen_ascii_chars().take(100).collect();
+
+ r.reseed((ReseedWithDefault, 3));
+
+ let string2: String = r.gen_ascii_chars().take(100).collect();
+ assert_eq!(string1, string2);
+ }
+
+ const FILL_BYTES_V_LEN: usize = 13579;
+ #[test]
+ fn test_rng_fill_bytes() {
+ let mut v = repeat(0u8).take(FILL_BYTES_V_LEN).collect::<Vec<_>>();
+ ::test::rng().fill_bytes(&mut v);
+
+ // Sanity test: if we've gotten here, `fill_bytes` has not infinitely
+ // recursed.
+ assert_eq!(v.len(), FILL_BYTES_V_LEN);
+
+ // To test that `fill_bytes` actually did something, check that the
+ // average of `v` is not 0.
+ let mut sum = 0.0;
+ for &x in v.iter() {
+ sum += x as f64;
+ }
+ assert!(sum / v.len() as f64 != 0.0);
+ }
+}
diff --git a/rand/src/seq.rs b/rand/src/seq.rs
new file mode 100644
index 0000000..a7889fe
--- /dev/null
+++ b/rand/src/seq.rs
@@ -0,0 +1,337 @@
+// Copyright 2017 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.
+
+//! Functions for randomly accessing and sampling sequences.
+
+use super::Rng;
+
+// This crate is only enabled when either std or alloc is available.
+// BTreeMap is not as fast in tests, but better than nothing.
+#[cfg(feature="std")] use std::collections::HashMap;
+#[cfg(not(feature="std"))] use alloc::btree_map::BTreeMap;
+
+#[cfg(not(feature="std"))] use alloc::Vec;
+
+/// Randomly sample `amount` elements from a finite iterator.
+///
+/// The following can be returned:
+/// - `Ok`: `Vec` of `amount` non-repeating randomly sampled elements. The order is not random.
+/// - `Err`: `Vec` of all the elements from `iterable` in sequential order. This happens when the
+/// length of `iterable` was less than `amount`. This is considered an error since exactly
+/// `amount` elements is typically expected.
+///
+/// This implementation uses `O(len(iterable))` time and `O(amount)` memory.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::{thread_rng, seq};
+///
+/// let mut rng = thread_rng();
+/// let sample = seq::sample_iter(&mut rng, 1..100, 5).unwrap();
+/// println!("{:?}", sample);
+/// ```
+pub fn sample_iter<T, I, R>(rng: &mut R, iterable: I, amount: usize) -> Result<Vec<T>, Vec<T>>
+ where I: IntoIterator<Item=T>,
+ R: Rng,
+{
+ let mut iter = iterable.into_iter();
+ let mut reservoir = Vec::with_capacity(amount);
+ reservoir.extend(iter.by_ref().take(amount));
+
+ // Continue unless the iterator was exhausted
+ //
+ // note: this prevents iterators that "restart" from causing problems.
+ // If the iterator stops once, then so do we.
+ if reservoir.len() == amount {
+ for (i, elem) in iter.enumerate() {
+ let k = rng.gen_range(0, i + 1 + amount);
+ if let Some(spot) = reservoir.get_mut(k) {
+ *spot = elem;
+ }
+ }
+ Ok(reservoir)
+ } else {
+ // Don't hang onto extra memory. There is a corner case where
+ // `amount` was much less than `len(iterable)`.
+ reservoir.shrink_to_fit();
+ Err(reservoir)
+ }
+}
+
+/// Randomly sample exactly `amount` values from `slice`.
+///
+/// The values are non-repeating and in random order.
+///
+/// This implementation uses `O(amount)` time and memory.
+///
+/// Panics if `amount > slice.len()`
+///
+/// # Example
+///
+/// ```rust
+/// use rand::{thread_rng, seq};
+///
+/// let mut rng = thread_rng();
+/// let values = vec![5, 6, 1, 3, 4, 6, 7];
+/// println!("{:?}", seq::sample_slice(&mut rng, &values, 3));
+/// ```
+pub fn sample_slice<R, T>(rng: &mut R, slice: &[T], amount: usize) -> Vec<T>
+ where R: Rng,
+ T: Clone
+{
+ let indices = sample_indices(rng, slice.len(), amount);
+
+ let mut out = Vec::with_capacity(amount);
+ out.extend(indices.iter().map(|i| slice[*i].clone()));
+ out
+}
+
+/// Randomly sample exactly `amount` references from `slice`.
+///
+/// The references are non-repeating and in random order.
+///
+/// This implementation uses `O(amount)` time and memory.
+///
+/// Panics if `amount > slice.len()`
+///
+/// # Example
+///
+/// ```rust
+/// use rand::{thread_rng, seq};
+///
+/// let mut rng = thread_rng();
+/// let values = vec![5, 6, 1, 3, 4, 6, 7];
+/// println!("{:?}", seq::sample_slice_ref(&mut rng, &values, 3));
+/// ```
+pub fn sample_slice_ref<'a, R, T>(rng: &mut R, slice: &'a [T], amount: usize) -> Vec<&'a T>
+ where R: Rng
+{
+ let indices = sample_indices(rng, slice.len(), amount);
+
+ let mut out = Vec::with_capacity(amount);
+ out.extend(indices.iter().map(|i| &slice[*i]));
+ out
+}
+
+/// Randomly sample exactly `amount` indices from `0..length`.
+///
+/// The values are non-repeating and in random order.
+///
+/// This implementation uses `O(amount)` time and memory.
+///
+/// This method is used internally by the slice sampling methods, but it can sometimes be useful to
+/// have the indices themselves so this is provided as an alternative.
+///
+/// Panics if `amount > length`
+pub fn sample_indices<R>(rng: &mut R, length: usize, amount: usize) -> Vec<usize>
+ where R: Rng,
+{
+ if amount > length {
+ panic!("`amount` must be less than or equal to `slice.len()`");
+ }
+
+ // We are going to have to allocate at least `amount` for the output no matter what. However,
+ // if we use the `cached` version we will have to allocate `amount` as a HashMap as well since
+ // it inserts an element for every loop.
+ //
+ // Therefore, if `amount >= length / 2` then inplace will be both faster and use less memory.
+ // In fact, benchmarks show the inplace version is faster for length up to about 20 times
+ // faster than amount.
+ //
+ // TODO: there is probably even more fine-tuning that can be done here since
+ // `HashMap::with_capacity(amount)` probably allocates more than `amount` in practice,
+ // and a trade off could probably be made between memory/cpu, since hashmap operations
+ // are slower than array index swapping.
+ if amount >= length / 20 {
+ sample_indices_inplace(rng, length, amount)
+ } else {
+ sample_indices_cache(rng, length, amount)
+ }
+}
+
+/// Sample an amount of indices using an inplace partial fisher yates method.
+///
+/// This allocates the entire `length` of indices and randomizes only the first `amount`.
+/// It then truncates to `amount` and returns.
+///
+/// This is better than using a HashMap "cache" when `amount >= length / 2` since it does not
+/// require allocating an extra cache and is much faster.
+fn sample_indices_inplace<R>(rng: &mut R, length: usize, amount: usize) -> Vec<usize>
+ where R: Rng,
+{
+ debug_assert!(amount <= length);
+ let mut indices: Vec<usize> = Vec::with_capacity(length);
+ indices.extend(0..length);
+ for i in 0..amount {
+ let j: usize = rng.gen_range(i, length);
+ let tmp = indices[i];
+ indices[i] = indices[j];
+ indices[j] = tmp;
+ }
+ indices.truncate(amount);
+ debug_assert_eq!(indices.len(), amount);
+ indices
+}
+
+
+/// This method performs a partial fisher-yates on a range of indices using a HashMap
+/// as a cache to record potential collisions.
+///
+/// The cache avoids allocating the entire `length` of values. This is especially useful when
+/// `amount <<< length`, i.e. select 3 non-repeating from 1_000_000
+fn sample_indices_cache<R>(
+ rng: &mut R,
+ length: usize,
+ amount: usize,
+) -> Vec<usize>
+ where R: Rng,
+{
+ debug_assert!(amount <= length);
+ #[cfg(feature="std")] let mut cache = HashMap::with_capacity(amount);
+ #[cfg(not(feature="std"))] let mut cache = BTreeMap::new();
+ let mut out = Vec::with_capacity(amount);
+ for i in 0..amount {
+ let j: usize = rng.gen_range(i, length);
+
+ // equiv: let tmp = slice[i];
+ let tmp = match cache.get(&i) {
+ Some(e) => *e,
+ None => i,
+ };
+
+ // equiv: slice[i] = slice[j];
+ let x = match cache.get(&j) {
+ Some(x) => *x,
+ None => j,
+ };
+
+ // equiv: slice[j] = tmp;
+ cache.insert(j, tmp);
+
+ // note that in the inplace version, slice[i] is automatically "returned" value
+ out.push(x);
+ }
+ debug_assert_eq!(out.len(), amount);
+ out
+}
+
+#[cfg(test)]
+mod test {
+ use super::*;
+ use {thread_rng, XorShiftRng, SeedableRng};
+
+ #[test]
+ fn test_sample_iter() {
+ let min_val = 1;
+ let max_val = 100;
+
+ let mut r = thread_rng();
+ let vals = (min_val..max_val).collect::<Vec<i32>>();
+ let small_sample = sample_iter(&mut r, vals.iter(), 5).unwrap();
+ let large_sample = sample_iter(&mut r, vals.iter(), vals.len() + 5).unwrap_err();
+
+ assert_eq!(small_sample.len(), 5);
+ assert_eq!(large_sample.len(), vals.len());
+ // no randomization happens when amount >= len
+ assert_eq!(large_sample, vals.iter().collect::<Vec<_>>());
+
+ assert!(small_sample.iter().all(|e| {
+ **e >= min_val && **e <= max_val
+ }));
+ }
+ #[test]
+ fn test_sample_slice_boundaries() {
+ let empty: &[u8] = &[];
+
+ let mut r = thread_rng();
+
+ // sample 0 items
+ assert_eq!(sample_slice(&mut r, empty, 0), vec![]);
+ assert_eq!(sample_slice(&mut r, &[42, 2, 42], 0), vec![]);
+
+ // sample 1 item
+ assert_eq!(sample_slice(&mut r, &[42], 1), vec![42]);
+ let v = sample_slice(&mut r, &[1, 42], 1)[0];
+ assert!(v == 1 || v == 42);
+
+ // sample "all" the items
+ let v = sample_slice(&mut r, &[42, 133], 2);
+ assert!(v == vec![42, 133] || v == vec![133, 42]);
+
+ assert_eq!(sample_indices_inplace(&mut r, 0, 0), vec![]);
+ assert_eq!(sample_indices_inplace(&mut r, 1, 0), vec![]);
+ assert_eq!(sample_indices_inplace(&mut r, 1, 1), vec![0]);
+
+ assert_eq!(sample_indices_cache(&mut r, 0, 0), vec![]);
+ assert_eq!(sample_indices_cache(&mut r, 1, 0), vec![]);
+ assert_eq!(sample_indices_cache(&mut r, 1, 1), vec![0]);
+
+ // Make sure lucky 777's aren't lucky
+ let slice = &[42, 777];
+ let mut num_42 = 0;
+ let total = 1000;
+ for _ in 0..total {
+ let v = sample_slice(&mut r, slice, 1);
+ assert_eq!(v.len(), 1);
+ let v = v[0];
+ assert!(v == 42 || v == 777);
+ if v == 42 {
+ num_42 += 1;
+ }
+ }
+ let ratio_42 = num_42 as f64 / 1000 as f64;
+ assert!(0.4 <= ratio_42 || ratio_42 <= 0.6, "{}", ratio_42);
+ }
+
+ #[test]
+ fn test_sample_slice() {
+ let xor_rng = XorShiftRng::from_seed;
+
+ let max_range = 100;
+ let mut r = thread_rng();
+
+ for length in 1usize..max_range {
+ let amount = r.gen_range(0, length);
+ let seed: [u32; 4] = [
+ r.next_u32(), r.next_u32(), r.next_u32(), r.next_u32()
+ ];
+
+ println!("Selecting indices: len={}, amount={}, seed={:?}", length, amount, seed);
+
+ // assert that the two index methods give exactly the same result
+ let inplace = sample_indices_inplace(
+ &mut xor_rng(seed), length, amount);
+ let cache = sample_indices_cache(
+ &mut xor_rng(seed), length, amount);
+ assert_eq!(inplace, cache);
+
+ // assert the basics work
+ let regular = sample_indices(
+ &mut xor_rng(seed), length, amount);
+ assert_eq!(regular.len(), amount);
+ assert!(regular.iter().all(|e| *e < length));
+ assert_eq!(regular, inplace);
+
+ // also test that sampling the slice works
+ let vec: Vec<usize> = (0..length).collect();
+ {
+ let result = sample_slice(&mut xor_rng(seed), &vec, amount);
+ assert_eq!(result, regular);
+ }
+
+ {
+ let result = sample_slice_ref(&mut xor_rng(seed), &vec, amount);
+ let expected = regular.iter().map(|v| v).collect::<Vec<_>>();
+ assert_eq!(result, expected);
+ }
+ }
+ }
+}
generated by cgit v1.2.3 (git 2.46.0) at 2024-11-22 05:48:34 +0000