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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
// option. This file may not be copied, modified, or distributed
// except according to those terms.
//! [`Rng`] trait
use rand_core::{Error, RngCore};
use crate::distributions::uniform::{SampleRange, SampleUniform};
use crate::distributions::{self, 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 dyn 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.
/// When using `rustc` ≥ 1.51, enable the `min_const_gen` feature to support
/// arrays larger than 32 elements.
///
/// 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 given range.
///
/// 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.
///
/// Only `gen_range(low..high)` and `gen_range(low..=high)` are supported.
///
/// # Panics
///
/// Panics if the range is empty.
///
/// # Example
///
/// ```
/// use rand::{thread_rng, Rng};
///
/// let mut rng = thread_rng();
///
/// // Exclusive range
/// let n: u32 = rng.gen_range(0..10);
/// println!("{}", n);
/// let m: f64 = rng.gen_range(-40.0..1.3e5);
/// println!("{}", m);
///
/// // Inclusive range
/// let n: u32 = rng.gen_range(0..=10);
/// println!("{}", n);
/// ```
///
/// [`Uniform`]: distributions::uniform::Uniform
fn gen_range<T, R>(&mut self, range: R) -> T
where
T: SampleUniform,
R: SampleRange<T>
{
assert!(!range.is_empty(), "cannot sample empty range");
range.sample_single(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 mut rng = thread_rng();
///
/// // Vec of 16 x f32:
/// let v: Vec<f32> = (&mut rng).sample_iter(Standard).take(16).collect();
///
/// // String:
/// let s: String = (&mut rng).sample_iter(Alphanumeric)
/// .take(7)
/// .map(char::from)
/// .collect();
///
/// // Combined values
/// println!("{:?}", (&mut rng).sample_iter(Standard).take(5)
/// .collect::<Vec<(f64, bool)>>());
///
/// // Dice-rolling:
/// let die_range = Uniform::new_inclusive(1, 6);
/// let mut roll_die = (&mut 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 any type implementing [`Fill`] with random data
///
/// The distribution is expected to be uniform with portable results, but
/// this cannot be guaranteed for third-party implementations.
///
/// This is identical to [`try_fill`] except that it panics on error.
///
/// # 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: Fill + ?Sized>(&mut self, dest: &mut T) {
dest.try_fill(self).unwrap_or_else(|_| panic!("Rng::fill failed"))
}
/// Fill any type implementing [`Fill`] with random data
///
/// The distribution is expected to be uniform with portable results, but
/// this cannot be guaranteed for third-party implementations.
///
/// This is identical to [`fill`] except that it forwards 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: Fill + ?Sized>(&mut self, dest: &mut T) -> Result<(), Error> {
dest.try_fill(self)
}
/// 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
#[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
#[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 {}
/// Types which may be filled with random data
///
/// This trait allows arrays to be efficiently filled with random data.
///
/// Implementations are expected to be portable across machines unless
/// clearly documented otherwise (see the
pub trait Fill {
/// Fill self with random data
fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error>;
}
macro_rules! impl_fill_each {
() => {};
($t:ty) => {
impl Fill for [$t] {
fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
for elt in self.iter_mut() {
*elt = rng.gen();
}
Ok(())
}
}
};
($t:ty, $($tt:ty,)*) => {
impl_fill_each!($t);
impl_fill_each!($($tt,)*);
};
}
impl_fill_each!(bool, char, f32, f64,);
impl Fill for [u8] {
fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
rng.try_fill_bytes(self)
}
}
macro_rules! impl_fill {
() => {};
($t:ty) => {
impl Fill for [$t] {
#[inline(never)] // in micro benchmarks, this improves performance
fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
if self.len() > 0 {
rng.try_fill_bytes(unsafe {
slice::from_raw_parts_mut(self.as_mut_ptr()
as *mut u8,
self.len() * mem::size_of::<$t>()
)
})?;
for x in self {
*x = x.to_le();
}
}
Ok(())
}
}
impl Fill for [Wrapping<$t>] {
#[inline(never)]
fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
if self.len() > 0 {
rng.try_fill_bytes(unsafe {
slice::from_raw_parts_mut(self.as_mut_ptr()
as *mut u8,
self.len() * mem::size_of::<$t>()
)
})?;
for x in self {
*x = Wrapping(x.0.to_le());
}
}
Ok(())
}
}
};
($t:ty, $($tt:ty,)*) => {
impl_fill!($t);
// TODO: this could replace above impl once Rust #32463 is fixed
// impl_fill!(Wrapping<$t>);
impl_fill!($($tt,)*);
}
}
impl_fill!(u16, u32, u64, usize, u128,);
impl_fill!(i8, i16, i32, i64, isize, i128,);
#[cfg_attr(doc_cfg, doc(cfg(feature = "min_const_gen")))]
#[cfg(feature = "min_const_gen")]
impl<T, const N: usize> Fill for [T; N]
where [T]: Fill
{
fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
self[..].try_fill(rng)
}
}
#[cfg(not(feature = "min_const_gen"))]
macro_rules! impl_fill_arrays {
($n:expr,) => {};
($n:expr, $N:ident) => {
impl<T> Fill for [T; $n] where [T]: Fill {
fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
self[..].try_fill(rng)
}
}
};
($n:expr, $N:ident, $($NN:ident,)*) => {
impl_fill_arrays!($n, $N);
impl_fill_arrays!($n - 1, $($NN,)*);
};
(!div $n:expr,) => {};
(!div $n:expr, $N:ident, $($NN:ident,)*) => {
impl_fill_arrays!($n, $N);
impl_fill_arrays!(!div $n / 2, $($NN,)*);
};
}
#[cfg(not(feature = "min_const_gen"))]
#[rustfmt::skip]
impl_fill_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,);
#[cfg(not(feature = "min_const_gen"))]
impl_fill_arrays!(!div 4096, N,N,N,N,N,N,N,);
#[cfg(test)]
mod test {
use super::*;
use crate::test::rng;
use crate::rngs::mock::StepRng;
#[cfg(feature = "alloc")] use alloc::boxed::Box;
#[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);
// Check equivalence for generated floats
let mut array = [0f32; 2];
rng.fill(&mut array);
let gen: [f32; 2] = rng.gen();
assert_eq!(array, gen);
}
#[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_int() {
let mut r = rng(101);
for _ in 0..1000 {
let a = r.gen_range(-4711..17);
assert!((-4711..17).contains(&a));
let a: i8 = r.gen_range(-3..42);
assert!((-3..42).contains(&a));
let a: u16 = r.gen_range(10..99);
assert!((10..99).contains(&a));
let a: i32 = r.gen_range(-100..2000);
assert!((-100..2000).contains(&a));
let a: u32 = r.gen_range(12..=24);
assert!((12..=24).contains(&a));
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]
fn test_gen_range_float() {
let mut r = rng(101);
for _ in 0..1000 {
let a = r.gen_range(-4.5..1.7);
assert!((-4.5..1.7).contains(&a));
let a = r.gen_range(-1.1..=-0.3);
assert!((-1.1..=-0.3).contains(&a));
assert_eq!(r.gen_range(0.0f32..=0.0), 0.);
assert_eq!(r.gen_range(-11.0..=-11.0), -11.);
assert_eq!(r.gen_range(3_000_000.0..=3_000_000.0), 3_000_000.);
}
}
#[test]
#[should_panic]
fn test_gen_range_panic_int() {
#![allow(clippy::reversed_empty_ranges)]
let mut r = rng(102);
r.gen_range(5..-2);
}
#[test]
#[should_panic]
fn test_gen_range_panic_usize() {
#![allow(clippy::reversed_empty_ranges)]
let mut r = rng(103);
r.gen_range(5..2);
}
#[test]
fn test_gen_bool() {
#![allow(clippy::bool_assert_comparison)]
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_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);
}
}