Import rand-0.7.3
Change-Id: Ib8c6100a9c5f516a04a5c045c6303942cd6db316
diff --git a/src/lib.rs b/src/lib.rs
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+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013-2017 The Rust Project Developers.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! Utilities for random number generation
+//!
+//! Rand provides utilities to generate random numbers, to convert them to
+//! useful types and distributions, and some randomness-related algorithms.
+//!
+//! # Quick Start
+//!
+//! To get you started quickly, the easiest and highest-level way to get
+//! a random value is to use [`random()`]; alternatively you can use
+//! [`thread_rng()`]. The [`Rng`] trait provides a useful API on all RNGs, while
+//! the [`distributions`] and [`seq`] modules provide further
+//! functionality on top of RNGs.
+//!
+//! ```
+//! use rand::prelude::*;
+//!
+//! if rand::random() { // generates a boolean
+//! // Try printing a random unicode code point (probably a bad idea)!
+//! println!("char: {}", rand::random::<char>());
+//! }
+//!
+//! let mut rng = rand::thread_rng();
+//! let y: f64 = rng.gen(); // generates a float between 0 and 1
+//!
+//! let mut nums: Vec<i32> = (1..100).collect();
+//! nums.shuffle(&mut rng);
+//! ```
+//!
+//! # The Book
+//!
+//! For the user guide and futher documentation, please read
+//! [The Rust Rand Book](https://rust-random.github.io/book).
+
+#![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://rust-random.github.io/rand/"
+)]
+#![deny(missing_docs)]
+#![deny(missing_debug_implementations)]
+#![doc(test(attr(allow(unused_variables), deny(warnings))))]
+#![cfg_attr(not(feature = "std"), no_std)]
+#![cfg_attr(all(feature = "simd_support", feature = "nightly"), feature(stdsimd))]
+#![allow(
+ clippy::excessive_precision,
+ clippy::unreadable_literal,
+ clippy::float_cmp
+)]
+
+#[cfg(all(feature = "alloc", not(feature = "std")))] extern crate alloc;
+
+#[allow(unused)]
+macro_rules! trace { ($($x:tt)*) => (
+ #[cfg(feature = "log")] {
+ log::trace!($($x)*)
+ }
+) }
+#[allow(unused)]
+macro_rules! debug { ($($x:tt)*) => (
+ #[cfg(feature = "log")] {
+ log::debug!($($x)*)
+ }
+) }
+#[allow(unused)]
+macro_rules! info { ($($x:tt)*) => (
+ #[cfg(feature = "log")] {
+ log::info!($($x)*)
+ }
+) }
+#[allow(unused)]
+macro_rules! warn { ($($x:tt)*) => (
+ #[cfg(feature = "log")] {
+ log::warn!($($x)*)
+ }
+) }
+#[allow(unused)]
+macro_rules! error { ($($x:tt)*) => (
+ #[cfg(feature = "log")] {
+ log::error!($($x)*)
+ }
+) }
+
+// Re-exports from rand_core
+pub use rand_core::{CryptoRng, Error, RngCore, SeedableRng};
+
+// Public exports
+#[cfg(feature = "std")] pub use crate::rngs::thread::thread_rng;
+
+// Public modules
+pub mod distributions;
+pub mod prelude;
+pub mod rngs;
+pub mod seq;
+
+
+use crate::distributions::uniform::{SampleBorrow, SampleUniform, UniformSampler};
+use crate::distributions::{Distribution, Standard};
+use core::num::Wrapping;
+use core::{mem, slice};
+
+/// An automatically-implemented extension trait on [`RngCore`] providing high-level
+/// generic methods for sampling values and other convenience methods.
+///
+/// This is the primary trait to use when generating random values.
+///
+/// # Generic usage
+///
+/// The basic pattern is `fn foo<R: Rng + ?Sized>(rng: &mut R)`. Some
+/// things are worth noting here:
+///
+/// - Since `Rng: RngCore` and every `RngCore` implements `Rng`, it makes no
+/// difference whether we use `R: Rng` or `R: RngCore`.
+/// - The `+ ?Sized` un-bounding allows functions to be called directly on
+/// type-erased references; i.e. `foo(r)` where `r: &mut RngCore`. Without
+/// this it would be necessary to write `foo(&mut r)`.
+///
+/// An alternative pattern is possible: `fn foo<R: Rng>(rng: R)`. This has some
+/// trade-offs. It allows the argument to be consumed directly without a `&mut`
+/// (which is how `from_rng(thread_rng())` works); also it still works directly
+/// on references (including type-erased references). Unfortunately within the
+/// function `foo` it is not known whether `rng` is a reference type or not,
+/// hence many uses of `rng` require an extra reference, either explicitly
+/// (`distr.sample(&mut rng)`) or implicitly (`rng.gen()`); one may hope the
+/// optimiser can remove redundant references later.
+///
+/// Example:
+///
+/// ```
+/// # use rand::thread_rng;
+/// use rand::Rng;
+///
+/// fn foo<R: Rng + ?Sized>(rng: &mut R) -> f32 {
+/// rng.gen()
+/// }
+///
+/// # let v = foo(&mut thread_rng());
+/// ```
+pub trait Rng: RngCore {
+ /// Return a random value supporting the [`Standard`] distribution.
+ ///
+ /// # Example
+ ///
+ /// ```
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let mut rng = thread_rng();
+ /// let x: u32 = rng.gen();
+ /// println!("{}", x);
+ /// println!("{:?}", rng.gen::<(f64, bool)>());
+ /// ```
+ ///
+ /// # Arrays and tuples
+ ///
+ /// The `rng.gen()` method is able to generate arrays (up to 32 elements)
+ /// and tuples (up to 12 elements), so long as all element types can be
+ /// generated.
+ ///
+ /// For arrays of integers, especially for those with small element types
+ /// (< 64 bit), it will likely be faster to instead use [`Rng::fill`].
+ ///
+ /// ```
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let mut rng = thread_rng();
+ /// let tuple: (u8, i32, char) = rng.gen(); // arbitrary tuple support
+ ///
+ /// let arr1: [f32; 32] = rng.gen(); // array construction
+ /// let mut arr2 = [0u8; 128];
+ /// rng.fill(&mut arr2); // array fill
+ /// ```
+ ///
+ /// [`Standard`]: distributions::Standard
+ #[inline]
+ fn gen<T>(&mut self) -> T
+ where Standard: Distribution<T> {
+ Standard.sample(self)
+ }
+
+ /// Generate a random value in the range [`low`, `high`), i.e. inclusive of
+ /// `low` and exclusive of `high`.
+ ///
+ /// This function is optimised for the case that only a single sample is
+ /// made from the given range. See also the [`Uniform`] distribution
+ /// type which may be faster if sampling from the same range repeatedly.
+ ///
+ /// # Panics
+ ///
+ /// Panics if `low >= high`.
+ ///
+ /// # Example
+ ///
+ /// ```
+ /// 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);
+ /// ```
+ ///
+ /// [`Uniform`]: distributions::uniform::Uniform
+ fn gen_range<T: SampleUniform, B1, B2>(&mut self, low: B1, high: B2) -> T
+ where
+ B1: SampleBorrow<T> + Sized,
+ B2: SampleBorrow<T> + Sized,
+ {
+ T::Sampler::sample_single(low, high, self)
+ }
+
+ /// Sample a new value, using the given distribution.
+ ///
+ /// ### Example
+ ///
+ /// ```
+ /// use rand::{thread_rng, Rng};
+ /// use rand::distributions::Uniform;
+ ///
+ /// let mut rng = thread_rng();
+ /// let x = rng.sample(Uniform::new(10u32, 15));
+ /// // Type annotation requires two types, the type and distribution; the
+ /// // distribution can be inferred.
+ /// let y = rng.sample::<u16, _>(Uniform::new(10, 15));
+ /// ```
+ fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T {
+ distr.sample(self)
+ }
+
+ /// Create an iterator that generates values using the given distribution.
+ ///
+ /// Note that this function takes its arguments by value. This works since
+ /// `(&mut R): Rng where R: Rng` and
+ /// `(&D): Distribution where D: Distribution`,
+ /// however borrowing is not automatic hence `rng.sample_iter(...)` may
+ /// need to be replaced with `(&mut rng).sample_iter(...)`.
+ ///
+ /// # Example
+ ///
+ /// ```
+ /// use rand::{thread_rng, Rng};
+ /// use rand::distributions::{Alphanumeric, Uniform, Standard};
+ ///
+ /// let rng = thread_rng();
+ ///
+ /// // Vec of 16 x f32:
+ /// let v: Vec<f32> = rng.sample_iter(Standard).take(16).collect();
+ ///
+ /// // String:
+ /// let s: String = rng.sample_iter(Alphanumeric).take(7).collect();
+ ///
+ /// // Combined values
+ /// println!("{:?}", rng.sample_iter(Standard).take(5)
+ /// .collect::<Vec<(f64, bool)>>());
+ ///
+ /// // Dice-rolling:
+ /// let die_range = Uniform::new_inclusive(1, 6);
+ /// let mut roll_die = rng.sample_iter(die_range);
+ /// while roll_die.next().unwrap() != 6 {
+ /// println!("Not a 6; rolling again!");
+ /// }
+ /// ```
+ fn sample_iter<T, D>(self, distr: D) -> distributions::DistIter<D, Self, T>
+ where
+ D: Distribution<T>,
+ Self: Sized,
+ {
+ distr.sample_iter(self)
+ }
+
+ /// Fill `dest` entirely with random bytes (uniform value distribution),
+ /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices
+ /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.).
+ ///
+ /// On big-endian platforms this performs byte-swapping to ensure
+ /// portability of results from reproducible generators.
+ ///
+ /// This uses [`fill_bytes`] internally which may handle some RNG errors
+ /// implicitly (e.g. waiting if the OS generator is not ready), but panics
+ /// on other errors. See also [`try_fill`] which returns errors.
+ ///
+ /// # Example
+ ///
+ /// ```
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let mut arr = [0i8; 20];
+ /// thread_rng().fill(&mut arr[..]);
+ /// ```
+ ///
+ /// [`fill_bytes`]: RngCore::fill_bytes
+ /// [`try_fill`]: Rng::try_fill
+ fn fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) {
+ self.fill_bytes(dest.as_byte_slice_mut());
+ dest.to_le();
+ }
+
+ /// Fill `dest` entirely with random bytes (uniform value distribution),
+ /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices
+ /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.).
+ ///
+ /// On big-endian platforms this performs byte-swapping to ensure
+ /// portability of results from reproducible generators.
+ ///
+ /// This is identical to [`fill`] except that it uses [`try_fill_bytes`]
+ /// internally and forwards RNG errors.
+ ///
+ /// # Example
+ ///
+ /// ```
+ /// # use rand::Error;
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// # fn try_inner() -> Result<(), Error> {
+ /// let mut arr = [0u64; 4];
+ /// thread_rng().try_fill(&mut arr[..])?;
+ /// # Ok(())
+ /// # }
+ ///
+ /// # try_inner().unwrap()
+ /// ```
+ ///
+ /// [`try_fill_bytes`]: RngCore::try_fill_bytes
+ /// [`fill`]: Rng::fill
+ fn try_fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) -> Result<(), Error> {
+ self.try_fill_bytes(dest.as_byte_slice_mut())?;
+ dest.to_le();
+ Ok(())
+ }
+
+ /// Return a bool with a probability `p` of being true.
+ ///
+ /// See also the [`Bernoulli`] distribution, which may be faster if
+ /// sampling from the same probability repeatedly.
+ ///
+ /// # Example
+ ///
+ /// ```
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let mut rng = thread_rng();
+ /// println!("{}", rng.gen_bool(1.0 / 3.0));
+ /// ```
+ ///
+ /// # Panics
+ ///
+ /// If `p < 0` or `p > 1`.
+ ///
+ /// [`Bernoulli`]: distributions::bernoulli::Bernoulli
+ #[inline]
+ fn gen_bool(&mut self, p: f64) -> bool {
+ let d = distributions::Bernoulli::new(p).unwrap();
+ self.sample(d)
+ }
+
+ /// Return a bool with a probability of `numerator/denominator` of being
+ /// true. I.e. `gen_ratio(2, 3)` has chance of 2 in 3, or about 67%, of
+ /// returning true. If `numerator == denominator`, then the returned value
+ /// is guaranteed to be `true`. If `numerator == 0`, then the returned
+ /// value is guaranteed to be `false`.
+ ///
+ /// See also the [`Bernoulli`] distribution, which may be faster if
+ /// sampling from the same `numerator` and `denominator` repeatedly.
+ ///
+ /// # Panics
+ ///
+ /// If `denominator == 0` or `numerator > denominator`.
+ ///
+ /// # Example
+ ///
+ /// ```
+ /// use rand::{thread_rng, Rng};
+ ///
+ /// let mut rng = thread_rng();
+ /// println!("{}", rng.gen_ratio(2, 3));
+ /// ```
+ ///
+ /// [`Bernoulli`]: distributions::bernoulli::Bernoulli
+ #[inline]
+ fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool {
+ let d = distributions::Bernoulli::from_ratio(numerator, denominator).unwrap();
+ self.sample(d)
+ }
+}
+
+impl<R: RngCore + ?Sized> Rng for R {}
+
+/// Trait for casting types to byte slices
+///
+/// This is used by the [`Rng::fill`] and [`Rng::try_fill`] methods.
+pub trait AsByteSliceMut {
+ /// Return a mutable reference to self as a byte slice
+ fn as_byte_slice_mut(&mut self) -> &mut [u8];
+
+ /// Call `to_le` on each element (i.e. byte-swap on Big Endian platforms).
+ fn to_le(&mut self);
+}
+
+impl AsByteSliceMut for [u8] {
+ fn as_byte_slice_mut(&mut self) -> &mut [u8] {
+ self
+ }
+
+ fn to_le(&mut self) {}
+}
+
+macro_rules! impl_as_byte_slice {
+ () => {};
+ ($t:ty) => {
+ impl AsByteSliceMut for [$t] {
+ fn as_byte_slice_mut(&mut self) -> &mut [u8] {
+ if self.len() == 0 {
+ unsafe {
+ // must not use null pointer
+ slice::from_raw_parts_mut(0x1 as *mut u8, 0)
+ }
+ } else {
+ unsafe {
+ slice::from_raw_parts_mut(self.as_mut_ptr()
+ as *mut u8,
+ self.len() * mem::size_of::<$t>()
+ )
+ }
+ }
+ }
+
+ fn to_le(&mut self) {
+ for x in self {
+ *x = x.to_le();
+ }
+ }
+ }
+
+ impl AsByteSliceMut for [Wrapping<$t>] {
+ fn as_byte_slice_mut(&mut self) -> &mut [u8] {
+ if self.len() == 0 {
+ unsafe {
+ // must not use null pointer
+ slice::from_raw_parts_mut(0x1 as *mut u8, 0)
+ }
+ } else {
+ unsafe {
+ slice::from_raw_parts_mut(self.as_mut_ptr()
+ as *mut u8,
+ self.len() * mem::size_of::<$t>()
+ )
+ }
+ }
+ }
+
+ fn to_le(&mut self) {
+ for x in self {
+ *x = Wrapping(x.0.to_le());
+ }
+ }
+ }
+ };
+ ($t:ty, $($tt:ty,)*) => {
+ impl_as_byte_slice!($t);
+ // TODO: this could replace above impl once Rust #32463 is fixed
+ // impl_as_byte_slice!(Wrapping<$t>);
+ impl_as_byte_slice!($($tt,)*);
+ }
+}
+
+impl_as_byte_slice!(u16, u32, u64, usize,);
+#[cfg(not(target_os = "emscripten"))]
+impl_as_byte_slice!(u128);
+impl_as_byte_slice!(i8, i16, i32, i64, isize,);
+#[cfg(not(target_os = "emscripten"))]
+impl_as_byte_slice!(i128);
+
+macro_rules! impl_as_byte_slice_arrays {
+ ($n:expr,) => {};
+ ($n:expr, $N:ident) => {
+ impl<T> AsByteSliceMut for [T; $n] where [T]: AsByteSliceMut {
+ fn as_byte_slice_mut(&mut self) -> &mut [u8] {
+ self[..].as_byte_slice_mut()
+ }
+
+ fn to_le(&mut self) {
+ self[..].to_le()
+ }
+ }
+ };
+ ($n:expr, $N:ident, $($NN:ident,)*) => {
+ impl_as_byte_slice_arrays!($n, $N);
+ impl_as_byte_slice_arrays!($n - 1, $($NN,)*);
+ };
+ (!div $n:expr,) => {};
+ (!div $n:expr, $N:ident, $($NN:ident,)*) => {
+ impl_as_byte_slice_arrays!($n, $N);
+ impl_as_byte_slice_arrays!(!div $n / 2, $($NN,)*);
+ };
+}
+#[rustfmt::skip]
+impl_as_byte_slice_arrays!(32, N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,);
+impl_as_byte_slice_arrays!(!div 4096, N,N,N,N,N,N,N,);
+
+/// Generates a random value using the thread-local random number generator.
+///
+/// This is simply a shortcut for `thread_rng().gen()`. See [`thread_rng`] for
+/// documentation of the entropy source and [`Standard`] for documentation of
+/// distributions and type-specific generation.
+///
+/// # 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!");
+/// }
+/// ```
+///
+/// If you're calling `random()` in a loop, caching the generator as in the
+/// following example can increase performance.
+///
+/// ```
+/// 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();
+/// }
+/// ```
+///
+/// [`Standard`]: distributions::Standard
+#[cfg(feature = "std")]
+#[inline]
+pub fn random<T>() -> T
+where Standard: Distribution<T> {
+ thread_rng().gen()
+}
+
+#[cfg(test)]
+mod test {
+ use super::*;
+ use crate::rngs::mock::StepRng;
+ #[cfg(all(not(feature = "std"), feature = "alloc"))] use alloc::boxed::Box;
+
+ /// Construct a deterministic RNG with the given seed
+ pub fn rng(seed: u64) -> impl RngCore {
+ // For tests, we want a statistically good, fast, reproducible RNG.
+ // PCG32 will do fine, and will be easy to embed if we ever need to.
+ const INC: u64 = 11634580027462260723;
+ rand_pcg::Pcg32::new(seed, INC)
+ }
+
+ #[test]
+ fn test_fill_bytes_default() {
+ let mut r = StepRng::new(0x11_22_33_44_55_66_77_88, 0);
+
+ // 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 buffer = [0u8; 87];
+ let v = &mut buffer[0..n];
+ r.fill_bytes(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_fill() {
+ let x = 9041086907909331047; // a random u64
+ let mut rng = StepRng::new(x, 0);
+
+ // Convert to byte sequence and back to u64; byte-swap twice if BE.
+ let mut array = [0u64; 2];
+ rng.fill(&mut array[..]);
+ assert_eq!(array, [x, x]);
+ assert_eq!(rng.next_u64(), x);
+
+ // Convert to bytes then u32 in LE order
+ let mut array = [0u32; 2];
+ rng.fill(&mut array[..]);
+ assert_eq!(array, [x as u32, (x >> 32) as u32]);
+ assert_eq!(rng.next_u32(), x as u32);
+
+ // Check equivalence using wrapped arrays
+ let mut warray = [Wrapping(0u32); 2];
+ rng.fill(&mut warray[..]);
+ assert_eq!(array[0], warray[0].0);
+ assert_eq!(array[1], warray[1].0);
+ }
+
+ #[test]
+ fn test_fill_empty() {
+ let mut array = [0u32; 0];
+ let mut rng = StepRng::new(0, 1);
+ rng.fill(&mut array);
+ rng.fill(&mut array[..]);
+ }
+
+ #[test]
+ fn test_gen_range() {
+ let mut r = rng(101);
+ for _ in 0..1000 {
+ let a = r.gen_range(-4711, 17);
+ assert!(a >= -4711 && a < 17);
+ let a = r.gen_range(-3i8, 42);
+ assert!(a >= -3i8 && a < 42i8);
+ let a = r.gen_range(&10u16, 99);
+ assert!(a >= 10u16 && a < 99u16);
+ let a = r.gen_range(-100i32, &2000);
+ assert!(a >= -100i32 && a < 2000i32);
+ let a = r.gen_range(&12u32, &24u32);
+ assert!(a >= 12u32 && a < 24u32);
+
+ assert_eq!(r.gen_range(0u32, 1), 0u32);
+ assert_eq!(r.gen_range(-12i64, -11), -12i64);
+ 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 = rng(102);
+ r.gen_range(5, -2);
+ }
+
+ #[test]
+ #[should_panic]
+ fn test_gen_range_panic_usize() {
+ let mut r = rng(103);
+ r.gen_range(5, 2);
+ }
+
+ #[test]
+ fn test_gen_bool() {
+ let mut r = rng(105);
+ for _ in 0..5 {
+ assert_eq!(r.gen_bool(0.0), false);
+ assert_eq!(r.gen_bool(1.0), true);
+ }
+ }
+
+ #[test]
+ fn test_rng_trait_object() {
+ use crate::distributions::{Distribution, Standard};
+ let mut rng = rng(109);
+ let mut r = &mut rng as &mut dyn RngCore;
+ r.next_u32();
+ r.gen::<i32>();
+ assert_eq!(r.gen_range(0, 1), 0);
+ let _c: u8 = Standard.sample(&mut r);
+ }
+
+ #[test]
+ #[cfg(feature = "alloc")]
+ fn test_rng_boxed_trait() {
+ use crate::distributions::{Distribution, Standard};
+ let rng = rng(110);
+ let mut r = Box::new(rng) as Box<dyn RngCore>;
+ r.next_u32();
+ r.gen::<i32>();
+ assert_eq!(r.gen_range(0, 1), 0);
+ let _c: u8 = Standard.sample(&mut r);
+ }
+
+ #[test]
+ #[cfg(feature = "std")]
+ 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]
+ #[cfg_attr(miri, ignore)] // Miri is too slow
+ fn test_gen_ratio_average() {
+ const NUM: u32 = 3;
+ const DENOM: u32 = 10;
+ const N: u32 = 100_000;
+
+ let mut sum: u32 = 0;
+ let mut rng = rng(111);
+ for _ in 0..N {
+ if rng.gen_ratio(NUM, DENOM) {
+ sum += 1;
+ }
+ }
+ // Have Binomial(N, NUM/DENOM) distribution
+ let expected = (NUM * N) / DENOM; // exact integer
+ assert!(((sum - expected) as i32).abs() < 500);
+ }
+}