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// Copyright 2013 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. //! //! # 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 //! `ReaderRng`. //! - 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 std::rand; //! use std::rand::Rng; //! //! let mut rng = rand::thread_rng(); //! if rng.gen() { // random bool //! println!("int: {}, uint: {}", rng.gen::<int>(), rng.gen::<uint>()) //! } //! ``` //! //! ```rust //! use std::rand; //! //! 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 std::rand; //! use std::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 std::rand; //! use std::rand::Rng; //! use std::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<uint>, 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: uint, choice: uint, rng: &mut R) -> uint { //! let choices = free_doors(&[car, choice]); //! rand::sample(rng, choices.into_iter(), 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: uint, open: uint) -> uint { //! free_doors(&[choice, open])[0] //! } //! //! fn free_doors(blocked: &[uint]) -> Vec<uint> { //! (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); //! } //! ``` #![unstable(feature = "rand")] #![deprecated(reason = "use the crates.io `rand` library instead", since = "1.0.0-alpha")] #![allow(deprecated)] use cell::RefCell; use clone::Clone; use old_io::IoResult; use iter::{Iterator, IteratorExt}; use mem; use rc::Rc; use result::Result::{Ok, Err}; use vec::Vec; #[cfg(target_pointer_width = "32")] use core_rand::IsaacRng as IsaacWordRng; #[cfg(target_pointer_width = "64")] use core_rand::Isaac64Rng as IsaacWordRng; pub use core_rand::{Rand, Rng, SeedableRng, Open01, Closed01}; pub use core_rand::{XorShiftRng, IsaacRng, Isaac64Rng, ChaChaRng}; pub use core_rand::{distributions, reseeding}; pub use rand::os::OsRng; pub mod os; pub mod reader; /// The standard RNG. This is designed to be efficient on the current /// platform. #[derive(Copy, Clone)] 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 `IoResult` return value. pub fn new() -> IoResult<StdRng> { OsRng::new().map(|mut r| StdRng { rng: r.gen() }) } } 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 read randomness from the operating system to seed the /// generator. pub fn weak_rng() -> XorShiftRng { match OsRng::new() { Ok(mut r) => r.gen(), Err(e) => panic!("weak_rng: failed to create seeded RNG: {:?}", e) } } /// Controls how the thread-local RNG is reseeded. struct ThreadRngReseeder; impl reseeding::Reseeder<StdRng> for ThreadRngReseeder { fn reseed(&mut self, rng: &mut StdRng) { *rng = match StdRng::new() { Ok(r) => r, Err(e) => panic!("could not reseed thread_rng: {}", e) } } } static THREAD_RNG_RESEED_THRESHOLD: usize = 32_768; type ThreadRngInner = reseeding::ReseedingRng<StdRng, ThreadRngReseeder>; /// The thread-local RNG. #[derive(Clone)] 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::<int>()`. /// /// The RNG provided will reseed itself from the operating system /// after generating a certain amount of randomness. /// /// 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`. 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!("could not initialize thread_rng: {}", 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()) } } 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 /// /// ``` /// use std::rand; /// /// let x = rand::random(); /// println!("{}", 2u8 * 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 std::rand; /// use std::rand::Rng; /// /// let mut v = vec![1, 2, 3]; /// /// for x in v.iter_mut() { /// *x = rand::random() /// } /// /// // would be faster as /// /// let mut rng = rand::thread_rng(); /// /// for x in v.iter_mut() { /// *x = rng.gen(); /// } /// ``` #[inline] pub fn random<T: Rand>() -> T { thread_rng().gen() } /// Randomly sample up to `amount` elements from an iterator. /// /// # Example /// /// ```rust /// use std::rand::{thread_rng, sample}; /// /// let mut rng = thread_rng(); /// let sample = sample(&mut rng, 1..100, 5); /// println!("{:?}", sample); /// ``` pub fn sample<T, I: Iterator<Item=T>, R: Rng>(rng: &mut R, mut iter: I, amount: usize) -> Vec<T> { let mut reservoir: Vec<T> = iter.by_ref().take(amount).collect(); for (i, elem) in iter.enumerate() { let k = rng.gen_range(0, i + 1 + amount); if k < amount { reservoir[k] = elem; } } return reservoir; } #[cfg(test)] mod test { use prelude::v1::*; use super::{Rng, thread_rng, random, SeedableRng, StdRng, sample}; use iter::{order, repeat}; 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 } #[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 { 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_fail] fn test_gen_range_panic_int() { let mut r = thread_rng(); r.gen_range(5, -2); } #[test] #[should_fail] fn test_gen_range_panic_uint() { let mut r = thread_rng(); r.gen_range(5, 2); } #[test] fn test_gen_f64() { let mut r = thread_rng(); let a = r.gen::<f64>(); let b = r.gen::<f64>(); debug!("{:?}", (a, b)); } #[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]).cloned(), Some(1)); let v: &[int] = &[]; assert_eq!(r.choose(v), None); } #[test] fn test_shuffle() { let mut r = thread_rng(); let empty: &mut [int] = &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::<int>(); 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 : uint = random(); let _f : f32 = random(); let _o : Option<Option<i8>> = random(); let _many : ((), (uint, int, Option<(u32, (bool,))>), (u8, i8, u16, i16, u32, i32, u64, i64), (f32, (f64, (f64,)))) = random(); } #[test] fn test_sample() { let min_val = 1; let max_val = 100; let mut r = thread_rng(); let vals = (min_val..max_val).collect::<Vec<int>>(); let small_sample = sample(&mut r, vals.iter(), 5); let large_sample = sample(&mut r, vals.iter(), vals.len() + 5); assert_eq!(small_sample.len(), 5); assert_eq!(large_sample.len(), vals.len()); assert!(small_sample.iter().all(|e| { **e >= min_val && **e <= max_val })); } #[test] fn test_std_rng_seeded() { let s = thread_rng().gen_iter::<uint>().take(256).collect::<Vec<uint>>(); let mut ra: StdRng = SeedableRng::from_seed(&*s); let mut rb: StdRng = SeedableRng::from_seed(&*s); assert!(order::equals(ra.gen_ascii_chars().take(100), rb.gen_ascii_chars().take(100))); } #[test] fn test_std_rng_reseed() { let s = thread_rng().gen_iter::<uint>().take(256).collect::<Vec<uint>>(); 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); } } #[cfg(test)] static RAND_BENCH_N: u64 = 100; #[cfg(test)] mod bench { extern crate test; use prelude::v1::*; use self::test::Bencher; use super::{XorShiftRng, StdRng, IsaacRng, Isaac64Rng, Rng, RAND_BENCH_N}; use super::{OsRng, weak_rng}; use mem::size_of; #[bench] fn rand_xorshift(b: &mut Bencher) { let mut rng: XorShiftRng = OsRng::new().unwrap().gen(); b.iter(|| { for _ in 0..RAND_BENCH_N { rng.gen::<uint>(); } }); b.bytes = size_of::<uint>() as u64 * RAND_BENCH_N; } #[bench] fn rand_isaac(b: &mut Bencher) { let mut rng: IsaacRng = OsRng::new().unwrap().gen(); b.iter(|| { for _ in 0..RAND_BENCH_N { rng.gen::<uint>(); } }); b.bytes = size_of::<uint>() as u64 * RAND_BENCH_N; } #[bench] fn rand_isaac64(b: &mut Bencher) { let mut rng: Isaac64Rng = OsRng::new().unwrap().gen(); b.iter(|| { for _ in 0..RAND_BENCH_N { rng.gen::<uint>(); } }); b.bytes = size_of::<uint>() as u64 * RAND_BENCH_N; } #[bench] fn rand_std(b: &mut Bencher) { let mut rng = StdRng::new().unwrap(); b.iter(|| { for _ in 0..RAND_BENCH_N { rng.gen::<uint>(); } }); b.bytes = size_of::<uint>() as u64 * RAND_BENCH_N; } #[bench] fn rand_shuffle_100(b: &mut Bencher) { let mut rng = weak_rng(); let x : &mut[uint] = &mut [1; 100]; b.iter(|| { rng.shuffle(x); }) } }