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 = "https://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 = "https://doc.rust-lang.org/nightly/",
26 html_playground_url = "https://play.rust-lang.org/")]
29 #![unstable(feature = "rand",
30 reason = "use `rand` from crates.io",
32 #![feature(core_float)]
33 #![feature(core_intrinsics)]
34 #![feature(core_slice_ext)]
36 #![feature(num_bits_bytes)]
37 #![feature(staged_api)]
39 #![feature(custom_attribute)]
40 #![allow(unused_attributes)]
42 #![cfg_attr(test, feature(test, rand, rustc_private, iter_order_deprecated))]
55 use core::marker::PhantomData;
57 pub use isaac::{IsaacRng, Isaac64Rng};
58 pub use chacha::ChaChaRng;
60 use distributions::{Range, IndependentSample};
61 use distributions::range::SampleRange;
64 const RAND_BENCH_N: u64 = 100;
66 pub mod distributions;
72 // Temporary trait to implement a few floating-point routines
73 // needed by librand; this is necessary because librand doesn't
74 // depend on libstd. This will go away when librand is integrated
76 trait FloatMath : Sized {
79 fn sqrt(self) -> Self;
80 fn powf(self, n: Self) -> Self;
83 impl FloatMath for f64 {
86 unsafe { intrinsics::expf64(self) }
91 unsafe { intrinsics::logf64(self) }
95 fn powf(self, n: f64) -> f64 {
96 unsafe { intrinsics::powf64(self, n) }
100 fn sqrt(self) -> f64 {
104 unsafe { intrinsics::sqrtf64(self) }
109 /// A type that can be randomly generated using an `Rng`.
111 pub trait Rand : Sized {
112 /// Generates a random instance of this type using the specified source of
114 fn rand<R: Rng>(rng: &mut R) -> Self;
117 /// A random number generator.
118 pub trait Rng : Sized {
119 /// Return the next random u32.
121 /// This rarely needs to be called directly, prefer `r.gen()` to
123 // FIXME #7771: Should be implemented in terms of next_u64
124 fn next_u32(&mut self) -> u32;
126 /// Return the next random u64.
128 /// By default this is implemented in terms of `next_u32`. An
129 /// implementation of this trait must provide at least one of
130 /// these two methods. Similarly to `next_u32`, this rarely needs
131 /// to be called directly, prefer `r.gen()` to `r.next_u64()`.
132 fn next_u64(&mut self) -> u64 {
133 ((self.next_u32() as u64) << 32) | (self.next_u32() as u64)
136 /// Return the next random f32 selected from the half-open
137 /// interval `[0, 1)`.
139 /// By default this is implemented in terms of `next_u32`, but a
140 /// random number generator which can generate numbers satisfying
141 /// the requirements directly can overload this for performance.
142 /// It is required that the return value lies in `[0, 1)`.
144 /// See `Closed01` for the closed interval `[0,1]`, and
145 /// `Open01` for the open interval `(0,1)`.
146 fn next_f32(&mut self) -> f32 {
147 const MANTISSA_BITS: usize = 24;
148 const IGNORED_BITS: usize = 8;
149 const SCALE: f32 = (1u64 << MANTISSA_BITS) as f32;
151 // using any more than `MANTISSA_BITS` bits will
152 // cause (e.g.) 0xffff_ffff to correspond to 1
153 // exactly, so we need to drop some (8 for f32, 11
154 // for f64) to guarantee the open end.
155 (self.next_u32() >> IGNORED_BITS) as f32 / SCALE
158 /// Return the next random f64 selected from the half-open
159 /// interval `[0, 1)`.
161 /// By default this is implemented in terms of `next_u64`, but a
162 /// random number generator which can generate numbers satisfying
163 /// the requirements directly can overload this for performance.
164 /// It is required that the return value lies in `[0, 1)`.
166 /// See `Closed01` for the closed interval `[0,1]`, and
167 /// `Open01` for the open interval `(0,1)`.
168 fn next_f64(&mut self) -> f64 {
169 const MANTISSA_BITS: usize = 53;
170 const IGNORED_BITS: usize = 11;
171 const SCALE: f64 = (1u64 << MANTISSA_BITS) as f64;
173 (self.next_u64() >> IGNORED_BITS) as f64 / SCALE
176 /// Fill `dest` with random data.
178 /// This has a default implementation in terms of `next_u64` and
179 /// `next_u32`, but should be overridden by implementations that
180 /// offer a more efficient solution than just calling those
181 /// methods repeatedly.
183 /// This method does *not* have a requirement to bear any fixed
184 /// relationship to the other methods, for example, it does *not*
185 /// have to result in the same output as progressively filling
186 /// `dest` with `self.gen::<u8>()`, and any such behaviour should
187 /// not be relied upon.
189 /// This method should guarantee that `dest` is entirely filled
190 /// with new data, and may panic if this is impossible
191 /// (e.g. reading past the end of a file that is being used as the
192 /// source of randomness).
193 fn fill_bytes(&mut self, dest: &mut [u8]) {
194 // this could, in theory, be done by transmuting dest to a
195 // [u64], but this is (1) likely to be undefined behaviour for
196 // LLVM, (2) has to be very careful about alignment concerns,
197 // (3) adds more `unsafe` that needs to be checked, (4)
198 // probably doesn't give much performance gain if
199 // optimisations are on.
204 // we could micro-optimise here by generating a u32 if
205 // we only need a few more bytes to fill the vector
207 num = self.next_u64();
211 *byte = (num & 0xff) as u8;
217 /// Return a random value of a `Rand` type.
219 fn gen<T: Rand>(&mut self) -> T {
223 /// Return an iterator that will yield an infinite number of randomly
225 fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> {
228 _marker: PhantomData,
232 /// Generate a random value in the range [`low`, `high`).
234 /// This is a convenience wrapper around
235 /// `distributions::Range`. If this function will be called
236 /// repeatedly with the same arguments, one should use `Range`, as
237 /// that will amortize the computations that allow for perfect
238 /// uniformity, as they only happen on initialization.
242 /// Panics if `low >= high`.
243 fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T {
244 assert!(low < high, "Rng.gen_range called with low >= high");
245 Range::new(low, high).ind_sample(self)
248 /// Return a bool with a 1 in n chance of true
249 fn gen_weighted_bool(&mut self, n: usize) -> bool {
250 n <= 1 || self.gen_range(0, n) == 0
253 /// Return an iterator of random characters from the set A-Z,a-z,0-9.
254 fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> {
255 AsciiGenerator { rng: self }
258 /// Return a random element from `values`.
260 /// Return `None` if `values` is empty.
261 fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> {
262 if values.is_empty() {
265 Some(&values[self.gen_range(0, values.len())])
269 /// Shuffle a mutable slice in place.
270 fn shuffle<T>(&mut self, values: &mut [T]) {
271 let mut i = values.len();
273 // invariant: elements with index >= i have been locked in place.
275 // lock element i in place.
276 values.swap(i, self.gen_range(0, i + 1));
281 /// Iterator which will generate a stream of random items.
283 /// This iterator is created via the `gen_iter` method on `Rng`.
284 pub struct Generator<'a, T, R: 'a> {
286 _marker: PhantomData<T>,
289 impl<'a, T: Rand, R: Rng> Iterator for Generator<'a, T, R> {
292 fn next(&mut self) -> Option<T> {
297 /// Iterator which will continuously generate random ascii characters.
299 /// This iterator is created via the `gen_ascii_chars` method on `Rng`.
300 pub struct AsciiGenerator<'a, R: 'a> {
304 impl<'a, R: Rng> Iterator for AsciiGenerator<'a, R> {
307 fn next(&mut self) -> Option<char> {
308 const GEN_ASCII_STR_CHARSET: &'static [u8] =
309 b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
310 abcdefghijklmnopqrstuvwxyz\
312 Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char)
316 /// A random number generator that can be explicitly seeded to produce
317 /// the same stream of randomness multiple times.
318 pub trait SeedableRng<Seed>: Rng {
319 /// Reseed an RNG with the given seed.
320 fn reseed(&mut self, Seed);
322 /// Create a new RNG with the given seed.
323 fn from_seed(seed: Seed) -> Self;
326 /// An Xorshift[1] random number
329 /// The Xorshift algorithm is not suitable for cryptographic purposes
330 /// but is very fast. If you do not know for sure that it fits your
331 /// requirements, use a more secure one such as `IsaacRng` or `OsRng`.
333 /// [1]: Marsaglia, George (July 2003). ["Xorshift
334 /// RNGs"](http://www.jstatsoft.org/v08/i14/paper). *Journal of
335 /// Statistical Software*. Vol. 8 (Issue 14).
337 pub struct XorShiftRng {
345 /// Creates a new XorShiftRng instance which is not seeded.
347 /// The initial values of this RNG are constants, so all generators created
348 /// by this function will yield the same stream of random numbers. It is
349 /// highly recommended that this is created through `SeedableRng` instead of
351 pub fn new_unseeded() -> XorShiftRng {
361 impl Rng for XorShiftRng {
363 fn next_u32(&mut self) -> u32 {
365 let t = x ^ (x << 11);
370 self.w = w ^ (w >> 19) ^ (t ^ (t >> 8));
375 impl SeedableRng<[u32; 4]> for XorShiftRng {
376 /// Reseed an XorShiftRng. This will panic if `seed` is entirely 0.
377 fn reseed(&mut self, seed: [u32; 4]) {
378 assert!(!seed.iter().all(|&x| x == 0),
379 "XorShiftRng.reseed called with an all zero seed.");
387 /// Create a new XorShiftRng. This will panic if `seed` is entirely 0.
388 fn from_seed(seed: [u32; 4]) -> XorShiftRng {
389 assert!(!seed.iter().all(|&x| x == 0),
390 "XorShiftRng::from_seed called with an all zero seed.");
401 impl Rand for XorShiftRng {
402 fn rand<R: Rng>(rng: &mut R) -> XorShiftRng {
403 let mut tuple: (u32, u32, u32, u32) = rng.gen();
404 while tuple == (0, 0, 0, 0) {
407 let (x, y, z, w) = tuple;
417 /// A wrapper for generating floating point numbers uniformly in the
418 /// open interval `(0,1)` (not including either endpoint).
420 /// Use `Closed01` for the closed interval `[0,1]`, and the default
421 /// `Rand` implementation for `f32` and `f64` for the half-open
423 pub struct Open01<F>(pub F);
425 /// A wrapper for generating floating point numbers uniformly in the
426 /// closed interval `[0,1]` (including both endpoints).
428 /// Use `Open01` for the closed interval `(0,1)`, and the default
429 /// `Rand` implementation of `f32` and `f64` for the half-open
431 pub struct Closed01<F>(pub F);
435 use std::__rand as rand;
437 pub struct MyRng<R> {
441 impl<R: rand::Rng> ::Rng for MyRng<R> {
442 fn next_u32(&mut self) -> u32 {
443 rand::Rng::next_u32(&mut self.inner)
447 pub fn rng() -> MyRng<rand::ThreadRng> {
448 MyRng { inner: rand::thread_rng() }
451 pub fn weak_rng() -> MyRng<rand::ThreadRng> {
452 MyRng { inner: rand::thread_rng() }