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Diffstat (limited to 'vendor/rand/src/rng.rs')
| -rw-r--r-- | vendor/rand/src/rng.rs | 600 |
1 files changed, 600 insertions, 0 deletions
diff --git a/vendor/rand/src/rng.rs b/vendor/rand/src/rng.rs new file mode 100644 index 000000000..79a9fbff4 --- /dev/null +++ b/vendor/rand/src/rng.rs @@ -0,0 +1,600 @@ +// 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. + +//! [`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 +/// [Chapter on Portability](https://rust-random.github.io/book/portability.html)). +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); + } +} |