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 #![crate_name = "rand"]
20 #![crate_type = "rlib"]
21 #![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png",
22 html_favicon_url = "https://doc.rust-lang.org/favicon.ico",
23 html_root_url = "https://doc.rust-lang.org/nightly/",
24 html_playground_url = "https://play.rust-lang.org/",
25 test(attr(deny(warnings))))]
27 #![unstable(feature = "rand",
28 reason = "use `rand` from crates.io",
30 #![feature(core_float)]
31 #![feature(core_intrinsics)]
32 #![feature(num_bits_bytes)]
33 #![feature(staged_api)]
35 #![feature(custom_attribute)]
36 #![allow(unused_attributes)]
38 #![cfg_attr(test, feature(test, rand, rustc_private, iter_order_deprecated))]
51 use core::marker::PhantomData;
53 pub use isaac::{IsaacRng, Isaac64Rng};
54 pub use chacha::ChaChaRng;
56 use distributions::{Range, IndependentSample};
57 use distributions::range::SampleRange;
60 const RAND_BENCH_N: u64 = 100;
62 pub mod distributions;
68 // Temporary trait to implement a few floating-point routines
69 // needed by librand; this is necessary because librand doesn't
70 // depend on libstd. This will go away when librand is integrated
72 trait FloatMath : Sized {
75 fn sqrt(self) -> Self;
76 fn powf(self, n: Self) -> Self;
79 impl FloatMath for f64 {
82 unsafe { intrinsics::expf64(self) }
87 unsafe { intrinsics::logf64(self) }
91 fn powf(self, n: f64) -> f64 {
92 unsafe { intrinsics::powf64(self, n) }
96 fn sqrt(self) -> f64 {
100 unsafe { intrinsics::sqrtf64(self) }
105 /// A type that can be randomly generated using an `Rng`.
107 pub trait Rand : Sized {
108 /// Generates a random instance of this type using the specified source of
110 fn rand<R: Rng>(rng: &mut R) -> Self;
113 /// A random number generator.
114 pub trait Rng : Sized {
115 /// Return the next random u32.
117 /// This rarely needs to be called directly, prefer `r.gen()` to
119 // FIXME #7771: Should be implemented in terms of next_u64
120 fn next_u32(&mut self) -> u32;
122 /// Return the next random u64.
124 /// By default this is implemented in terms of `next_u32`. An
125 /// implementation of this trait must provide at least one of
126 /// these two methods. Similarly to `next_u32`, this rarely needs
127 /// to be called directly, prefer `r.gen()` to `r.next_u64()`.
128 fn next_u64(&mut self) -> u64 {
129 ((self.next_u32() as u64) << 32) | (self.next_u32() as u64)
132 /// Return the next random f32 selected from the half-open
133 /// interval `[0, 1)`.
135 /// By default this is implemented in terms of `next_u32`, but a
136 /// random number generator which can generate numbers satisfying
137 /// the requirements directly can overload this for performance.
138 /// It is required that the return value lies in `[0, 1)`.
140 /// See `Closed01` for the closed interval `[0,1]`, and
141 /// `Open01` for the open interval `(0,1)`.
142 fn next_f32(&mut self) -> f32 {
143 const MANTISSA_BITS: usize = 24;
144 const IGNORED_BITS: usize = 8;
145 const SCALE: f32 = (1u64 << MANTISSA_BITS) as f32;
147 // using any more than `MANTISSA_BITS` bits will
148 // cause (e.g.) 0xffff_ffff to correspond to 1
149 // exactly, so we need to drop some (8 for f32, 11
150 // for f64) to guarantee the open end.
151 (self.next_u32() >> IGNORED_BITS) as f32 / SCALE
154 /// Return the next random f64 selected from the half-open
155 /// interval `[0, 1)`.
157 /// By default this is implemented in terms of `next_u64`, but a
158 /// random number generator which can generate numbers satisfying
159 /// the requirements directly can overload this for performance.
160 /// It is required that the return value lies in `[0, 1)`.
162 /// See `Closed01` for the closed interval `[0,1]`, and
163 /// `Open01` for the open interval `(0,1)`.
164 fn next_f64(&mut self) -> f64 {
165 const MANTISSA_BITS: usize = 53;
166 const IGNORED_BITS: usize = 11;
167 const SCALE: f64 = (1u64 << MANTISSA_BITS) as f64;
169 (self.next_u64() >> IGNORED_BITS) as f64 / SCALE
172 /// Fill `dest` with random data.
174 /// This has a default implementation in terms of `next_u64` and
175 /// `next_u32`, but should be overridden by implementations that
176 /// offer a more efficient solution than just calling those
177 /// methods repeatedly.
179 /// This method does *not* have a requirement to bear any fixed
180 /// relationship to the other methods, for example, it does *not*
181 /// have to result in the same output as progressively filling
182 /// `dest` with `self.gen::<u8>()`, and any such behaviour should
183 /// not be relied upon.
185 /// This method should guarantee that `dest` is entirely filled
186 /// with new data, and may panic if this is impossible
187 /// (e.g. reading past the end of a file that is being used as the
188 /// source of randomness).
189 fn fill_bytes(&mut self, dest: &mut [u8]) {
190 // this could, in theory, be done by transmuting dest to a
191 // [u64], but this is (1) likely to be undefined behaviour for
192 // LLVM, (2) has to be very careful about alignment concerns,
193 // (3) adds more `unsafe` that needs to be checked, (4)
194 // probably doesn't give much performance gain if
195 // optimisations are on.
200 // we could micro-optimise here by generating a u32 if
201 // we only need a few more bytes to fill the vector
203 num = self.next_u64();
207 *byte = (num & 0xff) as u8;
213 /// Return a random value of a `Rand` type.
215 fn gen<T: Rand>(&mut self) -> T {
219 /// Return an iterator that will yield an infinite number of randomly
221 fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> {
224 _marker: PhantomData,
228 /// Generate a random value in the range [`low`, `high`).
230 /// This is a convenience wrapper around
231 /// `distributions::Range`. If this function will be called
232 /// repeatedly with the same arguments, one should use `Range`, as
233 /// that will amortize the computations that allow for perfect
234 /// uniformity, as they only happen on initialization.
238 /// Panics if `low >= high`.
239 fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T {
240 assert!(low < high, "Rng.gen_range called with low >= high");
241 Range::new(low, high).ind_sample(self)
244 /// Return a bool with a 1 in n chance of true
245 fn gen_weighted_bool(&mut self, n: usize) -> bool {
246 n <= 1 || self.gen_range(0, n) == 0
249 /// Return an iterator of random characters from the set A-Z,a-z,0-9.
250 fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> {
251 AsciiGenerator { rng: self }
254 /// Return a random element from `values`.
256 /// Return `None` if `values` is empty.
257 fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> {
258 if values.is_empty() {
261 Some(&values[self.gen_range(0, values.len())])
265 /// Shuffle a mutable slice in place.
266 fn shuffle<T>(&mut self, values: &mut [T]) {
267 let mut i = values.len();
269 // invariant: elements with index >= i have been locked in place.
271 // lock element i in place.
272 values.swap(i, self.gen_range(0, i + 1));
277 /// Iterator which will generate a stream of random items.
279 /// This iterator is created via the `gen_iter` method on `Rng`.
280 pub struct Generator<'a, T, R: 'a> {
282 _marker: PhantomData<T>,
285 impl<'a, T: Rand, R: Rng> Iterator for Generator<'a, T, R> {
288 fn next(&mut self) -> Option<T> {
293 /// Iterator which will continuously generate random ascii characters.
295 /// This iterator is created via the `gen_ascii_chars` method on `Rng`.
296 pub struct AsciiGenerator<'a, R: 'a> {
300 impl<'a, R: Rng> Iterator for AsciiGenerator<'a, R> {
303 fn next(&mut self) -> Option<char> {
304 const GEN_ASCII_STR_CHARSET: &'static [u8] = b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
305 abcdefghijklmnopqrstuvwxyz\
307 Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char)
311 /// A random number generator that can be explicitly seeded to produce
312 /// the same stream of randomness multiple times.
313 pub trait SeedableRng<Seed>: Rng {
314 /// Reseed an RNG with the given seed.
315 fn reseed(&mut self, Seed);
317 /// Create a new RNG with the given seed.
318 fn from_seed(seed: Seed) -> Self;
321 /// An Xorshift[1] random number
324 /// The Xorshift algorithm is not suitable for cryptographic purposes
325 /// but is very fast. If you do not know for sure that it fits your
326 /// requirements, use a more secure one such as `IsaacRng` or `OsRng`.
328 /// [1]: Marsaglia, George (July 2003). ["Xorshift
329 /// RNGs"](http://www.jstatsoft.org/v08/i14/paper). *Journal of
330 /// Statistical Software*. Vol. 8 (Issue 14).
332 pub struct XorShiftRng {
340 /// Creates a new XorShiftRng instance which is not seeded.
342 /// The initial values of this RNG are constants, so all generators created
343 /// by this function will yield the same stream of random numbers. It is
344 /// highly recommended that this is created through `SeedableRng` instead of
346 pub fn new_unseeded() -> XorShiftRng {
356 impl Rng for XorShiftRng {
358 fn next_u32(&mut self) -> u32 {
360 let t = x ^ (x << 11);
365 self.w = w ^ (w >> 19) ^ (t ^ (t >> 8));
370 impl SeedableRng<[u32; 4]> for XorShiftRng {
371 /// Reseed an XorShiftRng. This will panic if `seed` is entirely 0.
372 fn reseed(&mut self, seed: [u32; 4]) {
373 assert!(!seed.iter().all(|&x| x == 0),
374 "XorShiftRng.reseed called with an all zero seed.");
382 /// Create a new XorShiftRng. This will panic if `seed` is entirely 0.
383 fn from_seed(seed: [u32; 4]) -> XorShiftRng {
384 assert!(!seed.iter().all(|&x| x == 0),
385 "XorShiftRng::from_seed called with an all zero seed.");
396 impl Rand for XorShiftRng {
397 fn rand<R: Rng>(rng: &mut R) -> XorShiftRng {
398 let mut tuple: (u32, u32, u32, u32) = rng.gen();
399 while tuple == (0, 0, 0, 0) {
402 let (x, y, z, w) = tuple;
412 /// A wrapper for generating floating point numbers uniformly in the
413 /// open interval `(0,1)` (not including either endpoint).
415 /// Use `Closed01` for the closed interval `[0,1]`, and the default
416 /// `Rand` implementation for `f32` and `f64` for the half-open
418 pub struct Open01<F>(pub F);
420 /// A wrapper for generating floating point numbers uniformly in the
421 /// closed interval `[0,1]` (including both endpoints).
423 /// Use `Open01` for the closed interval `(0,1)`, and the default
424 /// `Rand` implementation of `f32` and `f64` for the half-open
426 pub struct Closed01<F>(pub F);
430 use std::__rand as rand;
432 pub struct MyRng<R> {
436 impl<R: rand::Rng> ::Rng for MyRng<R> {
437 fn next_u32(&mut self) -> u32 {
438 rand::Rng::next_u32(&mut self.inner)
442 pub fn rng() -> MyRng<rand::ThreadRng> {
443 MyRng { inner: rand::thread_rng() }
446 pub fn weak_rng() -> MyRng<rand::ThreadRng> {
447 MyRng { inner: rand::thread_rng() }