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diff --git a/vendor/rand/src/lib.rs b/vendor/rand/src/lib.rs deleted file mode 100644 index 696ff6f..0000000 --- a/vendor/rand/src/lib.rs +++ /dev/null @@ -1,1220 +0,0 @@ -// Copyright 2013-2017 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. -//! -//! # Usage -//! -//! This crate is [on crates.io](https://crates.io/crates/rand) and can be -//! used by adding `rand` to the dependencies in your project's `Cargo.toml`. -//! -//! ```toml -//! [dependencies] -//! rand = "0.4" -//! ``` -//! -//! and this to your crate root: -//! -//! ```rust -//! extern crate rand; -//! ``` -//! -//! # 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 `ReadRng`. -//! - 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 rand::Rng; -//! -//! let mut rng = rand::thread_rng(); -//! if rng.gen() { // random bool -//! println!("i32: {}, u32: {}", rng.gen::<i32>(), rng.gen::<u32>()) -//! } -//! ``` -//! -//! ```rust -//! 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 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 rand::Rng; -//! use 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<u32>, 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: u32, choice: u32, rng: &mut R) -> u32 { -//! let choices = free_doors(&[car, choice]); -//! rand::seq::sample_slice(rng, &choices, 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: u32, open: u32) -> u32 { -//! free_doors(&[choice, open])[0] -//! } -//! -//! fn free_doors(blocked: &[u32]) -> Vec<u32> { -//! (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); -//! } -//! ``` - -#![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png", - html_favicon_url = "https://www.rust-lang.org/favicon.ico", - html_root_url = "https://docs.rs/rand/0.4")] - -#![deny(missing_debug_implementations)] - -#![cfg_attr(not(feature="std"), no_std)] -#![cfg_attr(all(feature="alloc", not(feature="std")), feature(alloc))] -#![cfg_attr(feature = "i128_support", feature(i128_type, i128))] - -#[cfg(feature="std")] extern crate std as core; -#[cfg(all(feature = "alloc", not(feature="std")))] extern crate alloc; - -#[cfg(target_env = "sgx")] -extern crate rdrand; - -#[cfg(target_env = "sgx")] -extern crate rand_core; - -use core::marker; -use core::mem; -#[cfg(feature="std")] use std::cell::RefCell; -#[cfg(feature="std")] use std::io; -#[cfg(feature="std")] use std::rc::Rc; - -// external rngs -pub use jitter::JitterRng; -#[cfg(feature="std")] pub use os::OsRng; - -// pseudo rngs -pub use isaac::{IsaacRng, Isaac64Rng}; -pub use chacha::ChaChaRng; -pub use prng::XorShiftRng; - -// local use declarations -#[cfg(target_pointer_width = "32")] -use prng::IsaacRng as IsaacWordRng; -#[cfg(target_pointer_width = "64")] -use prng::Isaac64Rng as IsaacWordRng; - -use distributions::{Range, IndependentSample}; -use distributions::range::SampleRange; - -// public modules -pub mod distributions; -pub mod jitter; -#[cfg(feature="std")] pub mod os; -#[cfg(feature="std")] pub mod read; -pub mod reseeding; -#[cfg(any(feature="std", feature = "alloc"))] pub mod seq; - -// These tiny modules are here to avoid API breakage, probably only temporarily -pub mod chacha { - //! The ChaCha random number generator. - pub use prng::ChaChaRng; -} -pub mod isaac { - //! The ISAAC random number generator. - pub use prng::{IsaacRng, Isaac64Rng}; -} - -// private modules -mod rand_impls; -mod prng; - - -/// A type that can be randomly generated using an `Rng`. -/// -/// ## Built-in Implementations -/// -/// This crate implements `Rand` for various primitive types. Assuming the -/// provided `Rng` is well-behaved, these implementations generate values with -/// the following ranges and distributions: -/// -/// * Integers (`i32`, `u32`, `isize`, `usize`, etc.): Uniformly distributed -/// over all values of the type. -/// * `char`: Uniformly distributed over all Unicode scalar values, i.e. all -/// code points in the range `0...0x10_FFFF`, except for the range -/// `0xD800...0xDFFF` (the surrogate code points). This includes -/// unassigned/reserved code points. -/// * `bool`: Generates `false` or `true`, each with probability 0.5. -/// * Floating point types (`f32` and `f64`): Uniformly distributed in the -/// half-open range `[0, 1)`. (The [`Open01`], [`Closed01`], [`Exp1`], and -/// [`StandardNormal`] wrapper types produce floating point numbers with -/// alternative ranges or distributions.) -/// -/// [`Open01`]: struct.Open01.html -/// [`Closed01`]: struct.Closed01.html -/// [`Exp1`]: distributions/exponential/struct.Exp1.html -/// [`StandardNormal`]: distributions/normal/struct.StandardNormal.html -/// -/// The following aggregate types also implement `Rand` as long as their -/// component types implement it: -/// -/// * Tuples and arrays: Each element of the tuple or array is generated -/// independently, using its own `Rand` implementation. -/// * `Option<T>`: Returns `None` with probability 0.5; otherwise generates a -/// random `T` and returns `Some(T)`. -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 { - /// Return the next random u32. - /// - /// This rarely needs to be called directly, prefer `r.gen()` to - /// `r.next_u32()`. - // FIXME #rust-lang/rfcs#628: 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)`. - /// - /// This uses a technique described by Saito and Matsumoto at - /// MCQMC'08. Given that the IEEE floating point numbers are - /// uniformly distributed over [1,2), we generate a number in - /// this range and then offset it onto the range [0,1). Our - /// choice of bits (masking v. shifting) is arbitrary and - /// should be immaterial for high quality generators. For low - /// quality generators (ex. LCG), prefer bitshifting due to - /// correlation between sequential low order bits. - /// - /// See: - /// A PRNG specialized in double precision floating point numbers using - /// an affine transition - /// - /// * <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/ARTICLES/dSFMT.pdf> - /// * <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/dSFMT-slide-e.pdf> - /// - /// 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 UPPER_MASK: u32 = 0x3F800000; - const LOWER_MASK: u32 = 0x7FFFFF; - let tmp = UPPER_MASK | (self.next_u32() & LOWER_MASK); - let result: f32 = unsafe { mem::transmute(tmp) }; - result - 1.0 - } - - /// 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 UPPER_MASK: u64 = 0x3FF0000000000000; - const LOWER_MASK: u64 = 0xFFFFFFFFFFFFF; - let tmp = UPPER_MASK | (self.next_u64() & LOWER_MASK); - let result: f64 = unsafe { mem::transmute(tmp) }; - result - 1.0 - } - - /// 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). - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let mut v = [0u8; 13579]; - /// thread_rng().fill_bytes(&mut v); - /// println!("{:?}", &v[..]); - /// ``` - 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.iter_mut() { - 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. - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let mut rng = thread_rng(); - /// let x: u32 = rng.gen(); - /// println!("{}", x); - /// println!("{:?}", rng.gen::<(f64, bool)>()); - /// ``` - #[inline(always)] - fn gen<T: Rand>(&mut self) -> T where Self: Sized { - Rand::rand(self) - } - - /// Return an iterator that will yield an infinite number of randomly - /// generated items. - /// - /// # Example - /// - /// ``` - /// use rand::{thread_rng, Rng}; - /// - /// let mut rng = thread_rng(); - /// let x = rng.gen_iter::<u32>().take(10).collect::<Vec<u32>>(); - /// println!("{:?}", x); - /// println!("{:?}", rng.gen_iter::<(f64, bool)>().take(5) - /// .collect::<Vec<(f64, bool)>>()); - /// ``` - fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> where Self: Sized { - Generator { rng: self, _marker: 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`. - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let mut rng = thread_rng(); - /// let n: u32 = rng.gen_range(0, 10); - /// println!("{}", n); - /// let m: f64 = rng.gen_range(-40.0f64, 1.3e5f64); - /// println!("{}", m); - /// ``` - fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T where Self: Sized { - 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 - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let mut rng = thread_rng(); - /// println!("{}", rng.gen_weighted_bool(3)); - /// ``` - fn gen_weighted_bool(&mut self, n: u32) -> bool where Self: Sized { - n <= 1 || self.gen_range(0, n) == 0 - } - - /// Return an iterator of random characters from the set A-Z,a-z,0-9. - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let s: String = thread_rng().gen_ascii_chars().take(10).collect(); - /// println!("{}", s); - /// ``` - fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> where Self: Sized { - AsciiGenerator { rng: self } - } - - /// Return a random element from `values`. - /// - /// Return `None` if `values` is empty. - /// - /// # Example - /// - /// ``` - /// use rand::{thread_rng, Rng}; - /// - /// let choices = [1, 2, 4, 8, 16, 32]; - /// let mut rng = thread_rng(); - /// println!("{:?}", rng.choose(&choices)); - /// assert_eq!(rng.choose(&choices[..0]), None); - /// ``` - fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> where Self: Sized { - if values.is_empty() { - None - } else { - Some(&values[self.gen_range(0, values.len())]) - } - } - - /// Return a mutable pointer to a random element from `values`. - /// - /// Return `None` if `values` is empty. - fn choose_mut<'a, T>(&mut self, values: &'a mut [T]) -> Option<&'a mut T> where Self: Sized { - if values.is_empty() { - None - } else { - let len = values.len(); - Some(&mut values[self.gen_range(0, len)]) - } - } - - /// Shuffle a mutable slice in place. - /// - /// This applies Durstenfeld's algorithm for the [Fisher–Yates shuffle](https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm) - /// which produces an unbiased permutation. - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let mut rng = thread_rng(); - /// let mut y = [1, 2, 3]; - /// rng.shuffle(&mut y); - /// println!("{:?}", y); - /// rng.shuffle(&mut y); - /// println!("{:?}", y); - /// ``` - fn shuffle<T>(&mut self, values: &mut [T]) where Self: Sized { - 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)); - } - } -} - -impl<'a, R: ?Sized> Rng for &'a mut R where R: Rng { - fn next_u32(&mut self) -> u32 { - (**self).next_u32() - } - - fn next_u64(&mut self) -> u64 { - (**self).next_u64() - } - - fn next_f32(&mut self) -> f32 { - (**self).next_f32() - } - - fn next_f64(&mut self) -> f64 { - (**self).next_f64() - } - - fn fill_bytes(&mut self, dest: &mut [u8]) { - (**self).fill_bytes(dest) - } -} - -#[cfg(feature="std")] -impl<R: ?Sized> Rng for Box<R> where R: Rng { - fn next_u32(&mut self) -> u32 { - (**self).next_u32() - } - - fn next_u64(&mut self) -> u64 { - (**self).next_u64() - } - - fn next_f32(&mut self) -> f32 { - (**self).next_f32() - } - - fn next_f64(&mut self) -> f64 { - (**self).next_f64() - } - - fn fill_bytes(&mut self, dest: &mut [u8]) { - (**self).fill_bytes(dest) - } -} - -/// Iterator which will generate a stream of random items. -/// -/// This iterator is created via the [`gen_iter`] method on [`Rng`]. -/// -/// [`gen_iter`]: trait.Rng.html#method.gen_iter -/// [`Rng`]: trait.Rng.html -#[derive(Debug)] -pub struct Generator<'a, T, R:'a> { - rng: &'a mut R, - _marker: marker::PhantomData<fn() -> 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`]. -/// -/// [`gen_ascii_chars`]: trait.Rng.html#method.gen_ascii_chars -/// [`Rng`]: trait.Rng.html -#[derive(Debug)] -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. - /// - /// # Example - /// - /// ```rust - /// use rand::{Rng, SeedableRng, StdRng}; - /// - /// let seed: &[_] = &[1, 2, 3, 4]; - /// let mut rng: StdRng = SeedableRng::from_seed(seed); - /// println!("{}", rng.gen::<f64>()); - /// rng.reseed(&[5, 6, 7, 8]); - /// println!("{}", rng.gen::<f64>()); - /// ``` - fn reseed(&mut self, Seed); - - /// Create a new RNG with the given seed. - /// - /// # Example - /// - /// ```rust - /// use rand::{Rng, SeedableRng, StdRng}; - /// - /// let seed: &[_] = &[1, 2, 3, 4]; - /// let mut rng: StdRng = SeedableRng::from_seed(seed); - /// println!("{}", rng.gen::<f64>()); - /// ``` - fn from_seed(seed: Seed) -> Self; -} - -/// 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)`. -/// -/// # Example -/// ```rust -/// use rand::{random, Open01}; -/// -/// let Open01(val) = random::<Open01<f32>>(); -/// println!("f32 from (0,1): {}", val); -/// ``` -#[derive(Debug)] -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)`. -/// -/// # Example -/// -/// ```rust -/// use rand::{random, Closed01}; -/// -/// let Closed01(val) = random::<Closed01<f32>>(); -/// println!("f32 from [0,1]: {}", val); -/// ``` -#[derive(Debug)] -pub struct Closed01<F>(pub F); - -/// The standard RNG. This is designed to be efficient on the current -/// platform. -#[derive(Copy, Clone, Debug)] -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 `io::Result` return value. - #[cfg(feature="std")] - pub fn new() -> io::Result<StdRng> { - match OsRng::new() { - Ok(mut r) => Ok(StdRng { rng: r.gen() }), - Err(e1) => { - match JitterRng::new() { - Ok(mut r) => Ok(StdRng { rng: r.gen() }), - Err(_) => { - Err(e1) - } - } - } - } - } -} - -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 seed the generator with randomness from thread_rng. -#[cfg(feature="std")] -pub fn weak_rng() -> XorShiftRng { - thread_rng().gen() -} - -/// Controls how the thread-local RNG is reseeded. -#[cfg(feature="std")] -#[derive(Debug)] -struct ThreadRngReseeder; - -#[cfg(feature="std")] -impl reseeding::Reseeder<StdRng> for ThreadRngReseeder { - fn reseed(&mut self, rng: &mut StdRng) { - match StdRng::new() { - Ok(r) => *rng = r, - Err(e) => panic!("No entropy available: {}", e), - } - } -} -#[cfg(feature="std")] -const THREAD_RNG_RESEED_THRESHOLD: u64 = 32_768; -#[cfg(feature="std")] -type ThreadRngInner = reseeding::ReseedingRng<StdRng, ThreadRngReseeder>; - -/// The thread-local RNG. -#[cfg(feature="std")] -#[derive(Clone, Debug)] -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::<i32>()`. -/// -/// After generating a certain amount of randomness, the RNG will reseed itself -/// from the operating system or, if the operating system RNG returns an error, -/// a seed based on the current system time. -/// -/// 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`. -#[cfg(feature="std")] -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!("No entropy available: {}", 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()) } -} - -#[cfg(feature="std")] -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 -/// -/// ``` -/// let x = rand::random::<u8>(); -/// println!("{}", 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 rand::Rng; -/// -/// let mut v = vec![1, 2, 3]; -/// -/// for x in v.iter_mut() { -/// *x = rand::random() -/// } -/// -/// // can be made faster by caching thread_rng -/// -/// let mut rng = rand::thread_rng(); -/// -/// for x in v.iter_mut() { -/// *x = rng.gen(); -/// } -/// ``` -#[cfg(feature="std")] -#[inline] -pub fn random<T: Rand>() -> T { - thread_rng().gen() -} - -/// DEPRECATED: use `seq::sample_iter` instead. -/// -/// Randomly sample up to `amount` elements from a finite iterator. -/// The order of elements in the sample is not random. -/// -/// # Example -/// -/// ```rust -/// use rand::{thread_rng, sample}; -/// -/// let mut rng = thread_rng(); -/// let sample = sample(&mut rng, 1..100, 5); -/// println!("{:?}", sample); -/// ``` -#[cfg(feature="std")] -#[inline(always)] -#[deprecated(since="0.4.0", note="renamed to seq::sample_iter")] -pub fn sample<T, I, R>(rng: &mut R, iterable: I, amount: usize) -> Vec<T> - where I: IntoIterator<Item=T>, - R: Rng, -{ - // the legacy sample didn't care whether amount was met - seq::sample_iter(rng, iterable, amount) - .unwrap_or_else(|e| e) -} - -#[cfg(test)] -mod test { - use super::{Rng, thread_rng, random, SeedableRng, StdRng, weak_rng}; - use std::iter::repeat; - - pub struct MyRng<R> { inner: R } - - impl<R: Rng> Rng for MyRng<R> { - fn next_u32(&mut self) -> u32 { - fn next<T: Rng>(t: &mut T) -> u32 { - t.next_u32() - } - next(&mut self.inner) - } - } - - pub fn rng() -> MyRng<::ThreadRng> { - MyRng { inner: ::thread_rng() } - } - - 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 - } - - pub fn iter_eq<I, J>(i: I, j: J) -> bool - where I: IntoIterator, - J: IntoIterator<Item=I::Item>, - I::Item: Eq - { - // make sure the iterators have equal length - let mut i = i.into_iter(); - let mut j = j.into_iter(); - loop { - match (i.next(), j.next()) { - (Some(ref ei), Some(ref ej)) if ei == ej => { } - (None, None) => return true, - _ => return false, - } - } - } - - #[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.iter() { - 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_panic] - fn test_gen_range_panic_int() { - let mut r = thread_rng(); - r.gen_range(5, -2); - } - - #[test] - #[should_panic] - fn test_gen_range_panic_usize() { - let mut r = thread_rng(); - r.gen_range(5, 2); - } - - #[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]).map(|&x|x), Some(1)); - - let v: &[isize] = &[]; - assert_eq!(r.choose(v), None); - } - - #[test] - fn test_shuffle() { - let mut r = thread_rng(); - let empty: &mut [isize] = &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::<i32>(); - 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_rng_trait_object() { - let mut rng = thread_rng(); - { - let mut r = &mut rng as &mut Rng; - r.next_u32(); - (&mut r).gen::<i32>(); - let mut v = [1, 1, 1]; - (&mut r).shuffle(&mut v); - let b: &[_] = &[1, 1, 1]; - assert_eq!(v, b); - assert_eq!((&mut r).gen_range(0, 1), 0); - } - { - let mut r = Box::new(rng) as Box<Rng>; - r.next_u32(); - r.gen::<i32>(); - 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 : usize = random(); - let _f : f32 = random(); - let _o : Option<Option<i8>> = random(); - let _many : ((), - (usize, - isize, - Option<(u32, (bool,))>), - (u8, i8, u16, i16, u32, i32, u64, i64), - (f32, (f64, (f64,)))) = random(); - } - - #[test] - fn test_std_rng_seeded() { - let s = thread_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>(); - let mut ra: StdRng = SeedableRng::from_seed(&s[..]); - let mut rb: StdRng = SeedableRng::from_seed(&s[..]); - assert!(iter_eq(ra.gen_ascii_chars().take(100), - rb.gen_ascii_chars().take(100))); - } - - #[test] - fn test_std_rng_reseed() { - let s = thread_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>(); - 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); - } - - #[test] - fn test_weak_rng() { - let s = weak_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>(); - let mut ra: StdRng = SeedableRng::from_seed(&s[..]); - let mut rb: StdRng = SeedableRng::from_seed(&s[..]); - assert!(iter_eq(ra.gen_ascii_chars().take(100), - rb.gen_ascii_chars().take(100))); - } -} |