diff options
Diffstat (limited to 'vendor/rand/src/lib.rs')
| -rw-r--r-- | vendor/rand/src/lib.rs | 214 |
1 files changed, 214 insertions, 0 deletions
diff --git a/vendor/rand/src/lib.rs b/vendor/rand/src/lib.rs new file mode 100644 index 000000000..6d8471801 --- /dev/null +++ b/vendor/rand/src/lib.rs @@ -0,0 +1,214 @@ +// Copyright 2018 Developers of the Rand project. +// Copyright 2013-2017 The Rust Project Developers. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license +// <LICENSE-MIT or https://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 +//! +//! Rand provides utilities to generate random numbers, to convert them to +//! useful types and distributions, and some randomness-related algorithms. +//! +//! # Quick Start +//! +//! To get you started quickly, the easiest and highest-level way to get +//! a random value is to use [`random()`]; alternatively you can use +//! [`thread_rng()`]. The [`Rng`] trait provides a useful API on all RNGs, while +//! the [`distributions`] and [`seq`] modules provide further +//! functionality on top of RNGs. +//! +//! ``` +//! use rand::prelude::*; +//! +//! if rand::random() { // generates a boolean +//! // Try printing a random unicode code point (probably a bad idea)! +//! println!("char: {}", rand::random::<char>()); +//! } +//! +//! let mut rng = rand::thread_rng(); +//! let y: f64 = rng.gen(); // generates a float between 0 and 1 +//! +//! let mut nums: Vec<i32> = (1..100).collect(); +//! nums.shuffle(&mut rng); +//! ``` +//! +//! # The Book +//! +//! For the user guide and further documentation, please read +//! [The Rust Rand Book](https://rust-random.github.io/book). + +#![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://rust-random.github.io/rand/" +)] +#![deny(missing_docs)] +#![deny(missing_debug_implementations)] +#![doc(test(attr(allow(unused_variables), deny(warnings))))] +#![no_std] +#![cfg_attr(feature = "simd_support", feature(stdsimd))] +#![cfg_attr(doc_cfg, feature(doc_cfg))] +#![allow( + clippy::float_cmp, + clippy::neg_cmp_op_on_partial_ord, +)] + +#[cfg(feature = "std")] extern crate std; +#[cfg(feature = "alloc")] extern crate alloc; + +#[allow(unused)] +macro_rules! trace { ($($x:tt)*) => ( + #[cfg(feature = "log")] { + log::trace!($($x)*) + } +) } +#[allow(unused)] +macro_rules! debug { ($($x:tt)*) => ( + #[cfg(feature = "log")] { + log::debug!($($x)*) + } +) } +#[allow(unused)] +macro_rules! info { ($($x:tt)*) => ( + #[cfg(feature = "log")] { + log::info!($($x)*) + } +) } +#[allow(unused)] +macro_rules! warn { ($($x:tt)*) => ( + #[cfg(feature = "log")] { + log::warn!($($x)*) + } +) } +#[allow(unused)] +macro_rules! error { ($($x:tt)*) => ( + #[cfg(feature = "log")] { + log::error!($($x)*) + } +) } + +// Re-exports from rand_core +pub use rand_core::{CryptoRng, Error, RngCore, SeedableRng}; + +// Public modules +pub mod distributions; +pub mod prelude; +mod rng; +pub mod rngs; +pub mod seq; + +// Public exports +#[cfg(all(feature = "std", feature = "std_rng"))] +pub use crate::rngs::thread::thread_rng; +pub use rng::{Fill, Rng}; + +#[cfg(all(feature = "std", feature = "std_rng"))] +use crate::distributions::{Distribution, Standard}; + +/// Generates a random value using the thread-local random number generator. +/// +/// This is simply a shortcut for `thread_rng().gen()`. See [`thread_rng`] for +/// documentation of the entropy source and [`Standard`] for documentation of +/// distributions and type-specific generation. +/// +/// # Provided implementations +/// +/// The following types have provided implementations that +/// 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)`. See notes below. +/// * Wrapping integers (`Wrapping<T>`), besides the type identical to their +/// normal integer variants. +/// +/// Also supported is the generation of the following +/// compound types where all component types are supported: +/// +/// * Tuples (up to 12 elements): each element is generated sequentially. +/// * Arrays (up to 32 elements): each element is generated sequentially; +/// see also [`Rng::fill`] which supports arbitrary array length for integer +/// types and tends to be faster for `u32` and smaller types. +/// * `Option<T>` first generates a `bool`, and if true generates and returns +/// `Some(value)` where `value: T`, otherwise returning `None`. +/// +/// # 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!"); +/// } +/// ``` +/// +/// If you're calling `random()` in a loop, caching the generator as in the +/// following example can increase performance. +/// +/// ``` +/// 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(); +/// } +/// ``` +/// +/// [`Standard`]: distributions::Standard +#[cfg(all(feature = "std", feature = "std_rng"))] +#[cfg_attr(doc_cfg, doc(cfg(all(feature = "std", feature = "std_rng"))))] +#[inline] +pub fn random<T>() -> T +where Standard: Distribution<T> { + thread_rng().gen() +} + +#[cfg(test)] +mod test { + use super::*; + + /// Construct a deterministic RNG with the given seed + pub fn rng(seed: u64) -> impl RngCore { + // For tests, we want a statistically good, fast, reproducible RNG. + // PCG32 will do fine, and will be easy to embed if we ever need to. + const INC: u64 = 11634580027462260723; + rand_pcg::Pcg32::new(seed, INC) + } + + #[test] + #[cfg(all(feature = "std", feature = "std_rng"))] + fn test_random() { + let _n: usize = random(); + let _f: f32 = random(); + let _o: Option<Option<i8>> = random(); + #[allow(clippy::type_complexity)] + let _many: ( + (), + (usize, isize, Option<(u32, (bool,))>), + (u8, i8, u16, i16, u32, i32, u64, i64), + (f32, (f64, (f64,))), + ) = random(); + } +} |