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// 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/0.3")] #![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))); } }