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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="alloc", not(feature="std")), feature(alloc))] #![cfg_attr(all(feature="simd_support", feature="nightly"), feature(stdsimd))] #[cfg(feature = "std")] extern crate core; #[cfg(all(feature = "alloc", not(feature="std")))] #[macro_use] extern crate alloc; #[cfg(feature="simd_support")] extern crate packed_simd; extern crate rand_jitter; #[cfg(feature = "rand_os")] extern crate rand_os; extern crate rand_core; extern crate rand_isaac; // only for deprecations extern crate rand_chacha; // only for deprecations extern crate rand_hc; extern crate rand_pcg; extern crate rand_xorshift; #[cfg(feature = "log")] #[macro_use] extern crate log; #[allow(unused)] #[cfg(not(feature = "log"))] macro_rules! trace { ($($x:tt)*) => () } #[allow(unused)] #[cfg(not(feature = "log"))] macro_rules! debug { ($($x:tt)*) => () } #[allow(unused)] #[cfg(not(feature = "log"))] macro_rules! info { ($($x:tt)*) => () } #[allow(unused)] #[cfg(not(feature = "log"))] macro_rules! warn { ($($x:tt)*) => () } #[allow(unused)] #[cfg(not(feature = "log"))] macro_rules! error { ($($x:tt)*) => () } // Re-exports from rand_core pub use rand_core::{RngCore, CryptoRng, SeedableRng}; pub use rand_core::{ErrorKind, Error}; // Public exports #[cfg(feature="std")] pub use rngs::thread::thread_rng; // Public modules pub mod distributions; pub mod prelude; #[deprecated(since="0.6.0")] pub mod prng; pub mod rngs; pub mod seq; //////////////////////////////////////////////////////////////////////////////// // Compatibility re-exports. Documentation is hidden; will be removed eventually. #[doc(hidden)] mod deprecated; #[allow(deprecated)] #[doc(hidden)] pub use deprecated::ReseedingRng; #[allow(deprecated)] #[cfg(feature="std")] #[doc(hidden)] pub use deprecated::EntropyRng; #[allow(deprecated)] #[cfg(feature="rand_os")] #[doc(hidden)] pub use deprecated::OsRng; #[allow(deprecated)] #[doc(hidden)] pub use deprecated::{ChaChaRng, IsaacRng, Isaac64Rng, XorShiftRng}; #[allow(deprecated)] #[doc(hidden)] pub use deprecated::StdRng; #[allow(deprecated)] #[doc(hidden)] pub mod jitter { pub use deprecated::JitterRng; pub use rngs::TimerError; } #[allow(deprecated)] #[cfg(feature="rand_os")] #[doc(hidden)] pub mod os { pub use deprecated::OsRng; } #[allow(deprecated)] #[doc(hidden)] pub mod chacha { pub use deprecated::ChaChaRng; } #[allow(deprecated)] #[doc(hidden)] pub mod isaac { pub use deprecated::{IsaacRng, Isaac64Rng}; } #[allow(deprecated)] #[cfg(feature="std")] #[doc(hidden)] pub mod read { pub use deprecated::ReadRng; } #[allow(deprecated)] #[cfg(feature="std")] #[doc(hidden)] pub use deprecated::ThreadRng; //////////////////////////////////////////////////////////////////////////////// use core::{mem, slice}; use distributions::{Distribution, Standard}; use distributions::uniform::{SampleUniform, UniformSampler, SampleBorrow}; /// 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. /// /// [`Standard`]: distributions::Standard /// /// # Example /// /// ``` /// use rand::{thread_rng, Rng}; /// /// let mut rng = thread_rng(); /// let x: u32 = rng.gen(); /// println!("{}", x); /// println!("{:?}", rng.gen::<(f64, bool)>()); /// ``` #[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. /// /// # 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> = thread_rng().sample_iter(&Standard).take(16).collect(); /// /// // String: /// let s: String = rng.sample_iter(&Alphanumeric).take(7).collect(); /// /// // Combined values /// println!("{:?}", thread_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<'a, T, D: Distribution<T>>(&'a mut self, distr: &'a D) -> distributions::DistIter<'a, D, Self, T> where 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 uses [`try_fill_bytes`] internally and forwards all RNG errors. In /// some cases errors may be resolvable; see [`ErrorKind`] and /// documentation for the RNG in use. If you do not plan to handle these /// errors you may prefer to use [`fill`]. /// /// # 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); 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); self.sample(d) } /// Return a random element from `values`. /// /// Deprecated: use [`seq::SliceRandom::choose`] instead. #[deprecated(since="0.6.0", note="use SliceRandom::choose instead")] fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> { use seq::SliceRandom; values.choose(self) } /// Return a mutable pointer to a random element from `values`. /// /// Deprecated: use [`seq::SliceRandom::choose_mut`] instead. #[deprecated(since="0.6.0", note="use SliceRandom::choose_mut instead")] fn choose_mut<'a, T>(&mut self, values: &'a mut [T]) -> Option<&'a mut T> { use seq::SliceRandom; values.choose_mut(self) } /// Shuffle a mutable slice in place. /// /// Deprecated: use [`seq::SliceRandom::shuffle`] instead. #[deprecated(since="0.6.0", note="use SliceRandom::shuffle instead")] fn shuffle<T>(&mut self, values: &mut [T]) { use seq::SliceRandom; values.shuffle(self) } } 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(&mut self[0] as *mut $t as *mut u8, self.len() * mem::size_of::<$t>() ) } } } fn to_le(&mut self) { for x in self { *x = x.to_le(); } } } } } impl_as_byte_slice!(u16); impl_as_byte_slice!(u32); impl_as_byte_slice!(u64); #[cfg(all(rustc_1_26, not(target_os = "emscripten")))] impl_as_byte_slice!(u128); impl_as_byte_slice!(usize); impl_as_byte_slice!(i8); impl_as_byte_slice!(i16); impl_as_byte_slice!(i32); impl_as_byte_slice!(i64); #[cfg(all(rustc_1_26, not(target_os = "emscripten")))] impl_as_byte_slice!(i128); impl_as_byte_slice!(isize); macro_rules! impl_as_byte_slice_arrays { ($n:expr,) => {}; ($n:expr, $N:ident, $($NN:ident,)*) => { impl_as_byte_slice_arrays!($n - 1, $($NN,)*); 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() } } }; (!div $n:expr,) => {}; (!div $n:expr, $N:ident, $($NN:ident,)*) => { impl_as_byte_slice_arrays!(!div $n / 2, $($NN,)*); 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() } } }; } 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,); /// A convenience extension to [`SeedableRng`] allowing construction from fresh /// entropy. This trait is automatically implemented for any PRNG implementing /// [`SeedableRng`] and is not intended to be implemented by users. /// /// This is equivalent to using `SeedableRng::from_rng(EntropyRng::new())` then /// unwrapping the result. /// /// Since this is convenient and secure, it is the recommended way to create /// PRNGs, though two alternatives may be considered: /// /// * Deterministic creation using [`SeedableRng::from_seed`] with a fixed seed /// * Seeding from `thread_rng`: `SeedableRng::from_rng(thread_rng())?`; /// this will usually be faster and should also be secure, but requires /// trusting one extra component. /// /// ## Example /// /// ``` /// use rand::{Rng, FromEntropy}; /// use rand::rngs::StdRng; /// /// let mut rng = StdRng::from_entropy(); /// println!("Random die roll: {}", rng.gen_range(1, 7)); /// ``` /// /// [`EntropyRng`]: rngs::EntropyRng #[cfg(feature="std")] pub trait FromEntropy: SeedableRng { /// Creates a new instance, automatically seeded with fresh entropy. /// /// Normally this will use `OsRng`, but if that fails `JitterRng` will be /// used instead. Both should be suitable for cryptography. It is possible /// that both entropy sources will fail though unlikely; failures would /// almost certainly be platform limitations or build issues, i.e. most /// applications targetting PC/mobile platforms should not need to worry /// about this failing. /// /// # Panics /// /// If all entropy sources fail this will panic. If you need to handle /// errors, use the following code, equivalent aside from error handling: /// /// ``` /// # use rand::Error; /// use rand::prelude::*; /// use rand::rngs::EntropyRng; /// /// # fn try_inner() -> Result<(), Error> { /// // This uses StdRng, but is valid for any R: SeedableRng /// let mut rng = StdRng::from_rng(EntropyRng::new())?; /// /// println!("random number: {}", rng.gen_range(1, 10)); /// # Ok(()) /// # } /// /// # try_inner().unwrap() /// ``` fn from_entropy() -> Self; } #[cfg(feature="std")] impl<R: SeedableRng> FromEntropy for R { fn from_entropy() -> R { R::from_rng(rngs::EntropyRng::new()).unwrap_or_else(|err| panic!("FromEntropy::from_entropy() failed: {}", err)) } } /// 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 rngs::mock::StepRng; use rngs::StdRng; use super::*; #[cfg(all(not(feature="std"), feature="alloc"))] use alloc::boxed::Box; pub struct TestRng<R> { inner: R } impl<R: RngCore> RngCore for TestRng<R> { 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, dest: &mut [u8]) { self.inner.fill_bytes(dest) } fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { self.inner.try_fill_bytes(dest) } } pub fn rng(seed: u64) -> TestRng<StdRng> { TestRng { inner: StdRng::seed_from_u64(seed) } } #[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); } #[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 distributions::{Distribution, Standard}; let mut rng = rng(109); let mut r = &mut rng as &mut 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 distributions::{Distribution, Standard}; let rng = rng(110); let mut r = Box::new(rng) as Box<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] 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); } }