1 // Copyright 2013-2014 The Rust Project Developers. See the COPYRIGHT
2 // file at the top-level directory of this distribution and at
3 // http://rust-lang.org/COPYRIGHT.
5 // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
6 // http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
7 // <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
8 // option. This file may not be copied, modified, or distributed
9 // except according to those terms.
11 //! Interface to random number generators in Rust.
13 //! This is an experimental library which lives underneath the standard library
14 //! in its dependency chain. This library is intended to define the interface
15 //! for random number generation and also provide utilities around doing so. It
16 //! is not recommended to use this library directly, but rather the official
17 //! interface through `std::rand`.
19 // Do not remove on snapshot creation. Needed for bootstrap. (Issue #22364)
20 #![cfg_attr(stage0, feature(custom_attribute))]
21 #![crate_name = "rand"]
22 #![crate_type = "rlib"]
23 #![doc(html_logo_url = "http://www.rust-lang.org/logos/rust-logo-128x128-blk.png",
24 html_favicon_url = "https://doc.rust-lang.org/favicon.ico",
25 html_root_url = "http://doc.rust-lang.org/nightly/",
26 html_playground_url = "http://play.rust-lang.org/")]
29 #![unstable(feature = "rand",
30 reason = "use `rand` from crates.io")]
33 #![feature(staged_api)]
36 #![cfg_attr(test, feature(test, rand, rustc_private))]
43 #[cfg(test)] #[macro_use] extern crate std;
44 #[cfg(test)] #[macro_use] extern crate log;
47 use core::marker::PhantomData;
49 pub use isaac::{IsaacRng, Isaac64Rng};
50 pub use chacha::ChaChaRng;
52 use distributions::{Range, IndependentSample};
53 use distributions::range::SampleRange;
56 const RAND_BENCH_N: u64 = 100;
58 pub mod distributions;
64 /// A type that can be randomly generated using an `Rng`.
65 pub trait Rand : Sized {
66 /// Generates a random instance of this type using the specified source of
68 fn rand<R: Rng>(rng: &mut R) -> Self;
71 /// A random number generator.
72 pub trait Rng : Sized {
73 /// Return the next random u32.
75 /// This rarely needs to be called directly, prefer `r.gen()` to
77 // FIXME #7771: Should be implemented in terms of next_u64
78 fn next_u32(&mut self) -> u32;
80 /// Return the next random u64.
82 /// By default this is implemented in terms of `next_u32`. An
83 /// implementation of this trait must provide at least one of
84 /// these two methods. Similarly to `next_u32`, this rarely needs
85 /// to be called directly, prefer `r.gen()` to `r.next_u64()`.
86 fn next_u64(&mut self) -> u64 {
87 ((self.next_u32() as u64) << 32) | (self.next_u32() as u64)
90 /// Return the next random f32 selected from the half-open
91 /// interval `[0, 1)`.
93 /// By default this is implemented in terms of `next_u32`, but a
94 /// random number generator which can generate numbers satisfying
95 /// the requirements directly can overload this for performance.
96 /// It is required that the return value lies in `[0, 1)`.
98 /// See `Closed01` for the closed interval `[0,1]`, and
99 /// `Open01` for the open interval `(0,1)`.
100 fn next_f32(&mut self) -> f32 {
101 const MANTISSA_BITS: usize = 24;
102 const IGNORED_BITS: usize = 8;
103 const SCALE: f32 = (1u64 << MANTISSA_BITS) as f32;
105 // using any more than `MANTISSA_BITS` bits will
106 // cause (e.g.) 0xffff_ffff to correspond to 1
107 // exactly, so we need to drop some (8 for f32, 11
108 // for f64) to guarantee the open end.
109 (self.next_u32() >> IGNORED_BITS) as f32 / SCALE
112 /// Return the next random f64 selected from the half-open
113 /// interval `[0, 1)`.
115 /// By default this is implemented in terms of `next_u64`, but a
116 /// random number generator which can generate numbers satisfying
117 /// the requirements directly can overload this for performance.
118 /// It is required that the return value lies in `[0, 1)`.
120 /// See `Closed01` for the closed interval `[0,1]`, and
121 /// `Open01` for the open interval `(0,1)`.
122 fn next_f64(&mut self) -> f64 {
123 const MANTISSA_BITS: usize = 53;
124 const IGNORED_BITS: usize = 11;
125 const SCALE: f64 = (1u64 << MANTISSA_BITS) as f64;
127 (self.next_u64() >> IGNORED_BITS) as f64 / SCALE
130 /// Fill `dest` with random data.
132 /// This has a default implementation in terms of `next_u64` and
133 /// `next_u32`, but should be overridden by implementations that
134 /// offer a more efficient solution than just calling those
135 /// methods repeatedly.
137 /// This method does *not* have a requirement to bear any fixed
138 /// relationship to the other methods, for example, it does *not*
139 /// have to result in the same output as progressively filling
140 /// `dest` with `self.gen::<u8>()`, and any such behaviour should
141 /// not be relied upon.
143 /// This method should guarantee that `dest` is entirely filled
144 /// with new data, and may panic if this is impossible
145 /// (e.g. reading past the end of a file that is being used as the
146 /// source of randomness).
147 fn fill_bytes(&mut self, dest: &mut [u8]) {
148 // this could, in theory, be done by transmuting dest to a
149 // [u64], but this is (1) likely to be undefined behaviour for
150 // LLVM, (2) has to be very careful about alignment concerns,
151 // (3) adds more `unsafe` that needs to be checked, (4)
152 // probably doesn't give much performance gain if
153 // optimisations are on.
158 // we could micro-optimise here by generating a u32 if
159 // we only need a few more bytes to fill the vector
161 num = self.next_u64();
165 *byte = (num & 0xff) as u8;
171 /// Return a random value of a `Rand` type.
173 fn gen<T: Rand>(&mut self) -> T {
177 /// Return an iterator that will yield an infinite number of randomly
179 fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> {
180 Generator { rng: self, _marker: PhantomData }
183 /// Generate a random value in the range [`low`, `high`).
185 /// This is a convenience wrapper around
186 /// `distributions::Range`. If this function will be called
187 /// repeatedly with the same arguments, one should use `Range`, as
188 /// that will amortize the computations that allow for perfect
189 /// uniformity, as they only happen on initialization.
193 /// Panics if `low >= high`.
194 fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T {
195 assert!(low < high, "Rng.gen_range called with low >= high");
196 Range::new(low, high).ind_sample(self)
199 /// Return a bool with a 1 in n chance of true
200 fn gen_weighted_bool(&mut self, n: usize) -> bool {
201 n <= 1 || self.gen_range(0, n) == 0
204 /// Return an iterator of random characters from the set A-Z,a-z,0-9.
205 fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> {
206 AsciiGenerator { rng: self }
209 /// Return a random element from `values`.
211 /// Return `None` if `values` is empty.
212 fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> {
213 if values.is_empty() {
216 Some(&values[self.gen_range(0, values.len())])
220 /// Shuffle a mutable slice in place.
221 fn shuffle<T>(&mut self, values: &mut [T]) {
222 let mut i = values.len();
224 // invariant: elements with index >= i have been locked in place.
226 // lock element i in place.
227 values.swap(i, self.gen_range(0, i + 1));
232 /// Iterator which will generate a stream of random items.
234 /// This iterator is created via the `gen_iter` method on `Rng`.
235 pub struct Generator<'a, T, R:'a> {
237 _marker: PhantomData<T>
240 impl<'a, T: Rand, R: Rng> Iterator for Generator<'a, T, R> {
243 fn next(&mut self) -> Option<T> {
248 /// Iterator which will continuously generate random ascii characters.
250 /// This iterator is created via the `gen_ascii_chars` method on `Rng`.
251 pub struct AsciiGenerator<'a, R:'a> {
255 impl<'a, R: Rng> Iterator for AsciiGenerator<'a, R> {
258 fn next(&mut self) -> Option<char> {
259 const GEN_ASCII_STR_CHARSET: &'static [u8] =
260 b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
261 abcdefghijklmnopqrstuvwxyz\
263 Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char)
267 /// A random number generator that can be explicitly seeded to produce
268 /// the same stream of randomness multiple times.
269 pub trait SeedableRng<Seed>: Rng {
270 /// Reseed an RNG with the given seed.
271 fn reseed(&mut self, Seed);
273 /// Create a new RNG with the given seed.
274 fn from_seed(seed: Seed) -> Self;
277 /// An Xorshift[1] random number
280 /// The Xorshift algorithm is not suitable for cryptographic purposes
281 /// but is very fast. If you do not know for sure that it fits your
282 /// requirements, use a more secure one such as `IsaacRng` or `OsRng`.
284 /// [1]: Marsaglia, George (July 2003). ["Xorshift
285 /// RNGs"](http://www.jstatsoft.org/v08/i14/paper). *Journal of
286 /// Statistical Software*. Vol. 8 (Issue 14).
288 pub struct XorShiftRng {
296 /// Creates a new XorShiftRng instance which is not seeded.
298 /// The initial values of this RNG are constants, so all generators created
299 /// by this function will yield the same stream of random numbers. It is
300 /// highly recommended that this is created through `SeedableRng` instead of
302 pub fn new_unseeded() -> XorShiftRng {
312 impl Rng for XorShiftRng {
314 fn next_u32(&mut self) -> u32 {
316 let t = x ^ (x << 11);
321 self.w = w ^ (w >> 19) ^ (t ^ (t >> 8));
326 impl SeedableRng<[u32; 4]> for XorShiftRng {
327 /// Reseed an XorShiftRng. This will panic if `seed` is entirely 0.
328 fn reseed(&mut self, seed: [u32; 4]) {
329 assert!(!seed.iter().all(|&x| x == 0),
330 "XorShiftRng.reseed called with an all zero seed.");
338 /// Create a new XorShiftRng. This will panic if `seed` is entirely 0.
339 fn from_seed(seed: [u32; 4]) -> XorShiftRng {
340 assert!(!seed.iter().all(|&x| x == 0),
341 "XorShiftRng::from_seed called with an all zero seed.");
352 impl Rand for XorShiftRng {
353 fn rand<R: Rng>(rng: &mut R) -> XorShiftRng {
354 let mut tuple: (u32, u32, u32, u32) = rng.gen();
355 while tuple == (0, 0, 0, 0) {
358 let (x, y, z, w) = tuple;
359 XorShiftRng { x: x, y: y, z: z, w: w }
363 /// A wrapper for generating floating point numbers uniformly in the
364 /// open interval `(0,1)` (not including either endpoint).
366 /// Use `Closed01` for the closed interval `[0,1]`, and the default
367 /// `Rand` implementation for `f32` and `f64` for the half-open
369 pub struct Open01<F>(pub F);
371 /// A wrapper for generating floating point numbers uniformly in the
372 /// closed interval `[0,1]` (including both endpoints).
374 /// Use `Open01` for the closed interval `(0,1)`, and the default
375 /// `Rand` implementation of `f32` and `f64` for the half-open
377 pub struct Closed01<F>(pub F);
381 use std::__rand as rand;
383 pub struct MyRng<R> { inner: R }
385 impl<R: rand::Rng> ::Rng for MyRng<R> {
386 fn next_u32(&mut self) -> u32 {
387 rand::Rng::next_u32(&mut self.inner)
391 pub fn rng() -> MyRng<rand::ThreadRng> {
392 MyRng { inner: rand::thread_rng() }
395 pub fn weak_rng() -> MyRng<rand::ThreadRng> {
396 MyRng { inner: rand::thread_rng() }