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// Copyright 2013-2014 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. //! Interface to random number generators in Rust. //! //! This is an experimental library which lives underneath the standard library //! in its dependency chain. This library is intended to define the interface //! for random number generation and also provide utilities around doing so. It //! is not recommended to use this library directly, but rather the official //! interface through `std::rand`. // Do not remove on snapshot creation. Needed for bootstrap. (Issue #22364) #![cfg_attr(stage0, feature(custom_attribute))] #![crate_name = "rand"] #![crate_type = "rlib"] #![doc(html_logo_url = "http://www.rust-lang.org/logos/rust-logo-128x128-blk.png", html_favicon_url = "http://www.rust-lang.org/favicon.ico", html_root_url = "http://doc.rust-lang.org/nightly/", html_playground_url = "http://play.rust-lang.org/")] #![no_std] #![staged_api] #![unstable(feature = "rand", reason = "use `rand` from crates.io")] #![feature(core)] #![feature(no_std)] #![feature(staged_api)] #![feature(step_by)] #![cfg_attr(test, feature(test, rand, rustc_private))] #![allow(deprecated)] #[macro_use] extern crate core; #[cfg(test)] #[macro_use] extern crate std; #[cfg(test)] #[macro_use] extern crate log; use core::prelude::*; use core::marker::PhantomData; pub use isaac::{IsaacRng, Isaac64Rng}; pub use chacha::ChaChaRng; use distributions::{Range, IndependentSample}; use distributions::range::SampleRange; #[cfg(test)] const RAND_BENCH_N: u64 = 100; pub mod distributions; pub mod isaac; pub mod chacha; pub mod reseeding; mod rand_impls; /// A type that can be randomly generated using an `Rng`. 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 : Sized { /// Return the next random u32. /// /// This rarely needs to be called directly, prefer `r.gen()` to /// `r.next_u32()`. // FIXME #7771: 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)`. /// /// 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 MANTISSA_BITS: usize = 24; const IGNORED_BITS: usize = 8; const SCALE: f32 = (1u64 << MANTISSA_BITS) as f32; // using any more than `MANTISSA_BITS` bits will // cause (e.g.) 0xffff_ffff to correspond to 1 // exactly, so we need to drop some (8 for f32, 11 // for f64) to guarantee the open end. (self.next_u32() >> IGNORED_BITS) as f32 / SCALE } /// 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 MANTISSA_BITS: usize = 53; const IGNORED_BITS: usize = 11; const SCALE: f64 = (1u64 << MANTISSA_BITS) as f64; (self.next_u64() >> IGNORED_BITS) as f64 / SCALE } /// 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). 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 { 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. #[inline(always)] fn gen<T: Rand>(&mut self) -> T { Rand::rand(self) } /// Return an iterator that will yield an infinite number of randomly /// generated items. fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> { Generator { rng: self, _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`. fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T { 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 fn gen_weighted_bool(&mut self, n: usize) -> bool { n <= 1 || self.gen_range(0, n) == 0 } /// Return an iterator of random characters from the set A-Z,a-z,0-9. fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> { AsciiGenerator { rng: self } } /// Return a random element from `values`. /// /// Return `None` if `values` is empty. fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> { if values.is_empty() { None } else { Some(&values[self.gen_range(0, values.len())]) } } /// Shuffle a mutable slice in place. fn shuffle<T>(&mut self, values: &mut [T]) { 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)); } } } /// Iterator which will generate a stream of random items. /// /// This iterator is created via the `gen_iter` method on `Rng`. pub struct Generator<'a, T, R:'a> { rng: &'a mut R, _marker: PhantomData<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`. 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. fn reseed(&mut self, Seed); /// Create a new RNG with the given seed. fn from_seed(seed: Seed) -> Self; } /// An Xorshift[1] random number /// generator. /// /// The Xorshift algorithm is not suitable for cryptographic purposes /// but is very fast. If you do not know for sure that it fits your /// requirements, use a more secure one such as `IsaacRng` or `OsRng`. /// /// [1]: Marsaglia, George (July 2003). ["Xorshift /// RNGs"](http://www.jstatsoft.org/v08/i14/paper). *Journal of /// Statistical Software*. Vol. 8 (Issue 14). #[derive(Clone)] pub struct XorShiftRng { x: u32, y: u32, z: u32, w: u32, } impl XorShiftRng { /// Creates a new XorShiftRng instance which is not seeded. /// /// The initial values of this RNG are constants, so all generators created /// by this function will yield the same stream of random numbers. It is /// highly recommended that this is created through `SeedableRng` instead of /// this function pub fn new_unseeded() -> XorShiftRng { XorShiftRng { x: 0x193a6754, y: 0xa8a7d469, z: 0x97830e05, w: 0x113ba7bb, } } } impl Rng for XorShiftRng { #[inline] fn next_u32(&mut self) -> u32 { let x = self.x; let t = x ^ (x << 11); self.x = self.y; self.y = self.z; self.z = self.w; let w = self.w; self.w = w ^ (w >> 19) ^ (t ^ (t >> 8)); self.w } } impl SeedableRng<[u32; 4]> for XorShiftRng { /// Reseed an XorShiftRng. This will panic if `seed` is entirely 0. fn reseed(&mut self, seed: [u32; 4]) { assert!(!seed.iter().all(|&x| x == 0), "XorShiftRng.reseed called with an all zero seed."); self.x = seed[0]; self.y = seed[1]; self.z = seed[2]; self.w = seed[3]; } /// Create a new XorShiftRng. This will panic if `seed` is entirely 0. fn from_seed(seed: [u32; 4]) -> XorShiftRng { assert!(!seed.iter().all(|&x| x == 0), "XorShiftRng::from_seed called with an all zero seed."); XorShiftRng { x: seed[0], y: seed[1], z: seed[2], w: seed[3] } } } impl Rand for XorShiftRng { fn rand<R: Rng>(rng: &mut R) -> XorShiftRng { let mut tuple: (u32, u32, u32, u32) = rng.gen(); while tuple == (0, 0, 0, 0) { tuple = rng.gen(); } let (x, y, z, w) = tuple; XorShiftRng { x: x, y: y, z: z, w: w } } } /// 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)`. 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)`. pub struct Closed01<F>(pub F); #[cfg(test)] mod test { use std::__rand as rand; pub struct MyRng<R> { inner: R } impl<R: rand::Rng> ::Rng for MyRng<R> { fn next_u32(&mut self) -> u32 { rand::Rng::next_u32(&mut self.inner) } } pub fn rng() -> MyRng<rand::ThreadRng> { MyRng { inner: rand::thread_rng() } } pub fn weak_rng() -> MyRng<rand::ThreadRng> { MyRng { inner: rand::thread_rng() } } }