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// Copyright 2013-2017 The Rust Project Developers. See the COPYRIGHT // file at the top-level directory of this distribution and at // https://rust-lang.org/COPYRIGHT. // // 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 //! //! ## Example //! //! ```rust //! // Rng is the main trait and needs to be imported: //! use rand::{Rng, thread_rng}; //! //! // thread_rng is often the most convenient source of randomness: //! let mut rng = rand::thread_rng(); //! if rng.gen() { // random bool //! let x: f64 = rng.gen(); // random number in range (0, 1) //! println!("x is: {}", x); //! println!("Number from 0 to 9: {}", rng.gen_range(0, 10)); //! } //! ``` //! //! The key function is [`Rng::gen()`]. It is polymorphic and so can be used to //! generate many types; the [`Uniform`] distribution carries the //! implementations. In some cases type annotation is required, e.g. //! `rng.gen::<f64>()`. //! //! # Getting random values //! //! The most convenient source of randomness is likely [`thread_rng`], which //! automatically initialises a fast algorithmic generator on first use per //! thread with thread-local storage. //! //! If one wants to obtain random data directly from an external source it is //! recommended to use [`EntropyRng`] which manages multiple available sources //! or [`OsRng`] which retrieves random data directly from the OS. It should be //! noted that this is significantly slower than using a local generator like //! [`thread_rng`] and potentially much slower if [`EntropyRng`] must fall back to //! [`JitterRng`] as a source. //! //! It is also common to use an algorithmic generator in local memory; this may //! be faster than `thread_rng` and provides more control. In this case //! [`StdRng`] — the generator behind [`thread_rng`] — and [`SmallRng`] — a //! small, fast, weak generator — are good choices; more options can be found in //! the [`prng`] module as well as in other crates. //! //! Local generators need to be seeded. It is recommended to use [`NewRng`] or //! to seed from a strong parent generator with [`from_rng`]: //! //! ``` //! // seed with fresh entropy: //! use rand::{StdRng, NewRng}; //! let mut rng = StdRng::new(); //! //! // seed from thread_rng: //! use rand::{SmallRng, SeedableRng, thread_rng}; //! let mut rng = SmallRng::from_rng(thread_rng()); //! ``` //! //! In case you specifically want to have a reproducible stream of "random" //! data (e.g. to procedurally generate a game world), select a named algorithm //! (i.e. not [`StdRng`]/[`SmallRng`] which may be adjusted in the future), and //! use [`SeedableRng::from_seed`] or a constructor specific to the generator //! (e.g. [`IsaacRng::new_from_u64`]). //! //! # Applying / converting random data //! //! The [`RngCore`] trait allows generators to implement a common interface for //! retrieving random data, but how should you use this? Typically users should //! use the [`Rng`] trait not [`RngCore`]; this provides more flexible ways to //! access the same data (e.g. `gen()` can output many more types than //! `next_u32()` and `next_u64()`; Rust's optimiser should eliminate any //! overhead). It also provides several useful algorithms, //! e.g. `gen_bool(p)` to generate events with weighted probability and //! `shuffle(&mut v[..])` to randomly-order a vector. //! //! The [`distributions`] module provides several more ways to convert random //! data to useful values, e.g. time of decay is often modelled with an //! exponential distribution, and the log-normal distribution provides a good //! model of many natural phenomona. //! //! The [`seq`] module has a few tools applicable to sliceable or iterable data. //! //! # Cryptographic security //! //! Security analysis requires a threat model and expert review; we can provide //! neither, but can provide some guidance. We assume that the goal is to //! obtain secret random data and that some source of secrets ("entropy") is //! available; that is, [`EntropyRng`] is functional. //! //! Potential threat: is the entropy source secure? The primary entropy source //! is [`OsRng`] which is simply a wrapper around the platform's native "secure //! entropy source"; usually this is available (outside of embedded platforms) //! and usually you can trust this (some caveats may apply; see [`OsRng`] doc). //! The fallback source used by [`EntropyRng`] is [`JitterRng`] which runs extensive //! tests on the quality of the CPU timer and is conservative in its estimates //! of the entropy harvested from each time sample; this makes it slow but very //! strong. Using [`EntropyRng`] directly should therefore be secure; the main //! reason not to is performance, which is why many applications use local //! algorithmic generators. //! //! Potential threat: are algorithmic generators predictable? Certainly some //! are; algorithmic generators fall broadly into two categories: those using a //! small amount of state (e.g. one to four 32- or 64-bit words) designed for //! non-security applications and those designed to be secure, typically with //! much larger state space and complex initialisation. The former should not be //! trusted to be secure, the latter may or may not have known weaknesses or //! may even have been proven secure under a specified adversarial model. We //! provide some notes on the security of the cryptographic algorithmic //! generators provided by this crate, [`Hc128Rng`] and [`ChaChaRng`]. Note that //! previously [`IsaacRng`] and [`Isaac64Rng`] were used as "reasonably strong //! generators"; these have no known weaknesses but also have no proofs of //! security, thus are not recommended for cryptographic uses. //! //! Potential threat: could the internal state of a cryptographic generator be //! leaked? This falls under the topic of "side channel attacks", and multiple //! variants are possible: the state of the generators being accidentally //! printed in log files or some other application output, the process's memory //! being copied somehow, the process being forked and both sub-processes //! outputting the same random sequence but such that one of those can be read; //! likely some other side-channel attacks are possible in some circumstances. //! It is typically impossible to prove immunity to all side-channel attacks, //! however some mitigation of known threats is usually possible, for example //! all generators implemented in this crate have a custom `Debug` //! implementation omitting all internal state, and [`ReseedingRng`] allows //! periodic reseeding such that a long-running process with leaked generator //! state should eventually recover to an unknown state. In the future we plan //! to add further mitigations; see issue #314. //! //! We provide the [`CryptoRng`] marker trait as an indication of which random //! generators/sources may be used for cryptographic applications; this should //! be considered advisory only does not imply any protection against //! side-channel attacks. //! //! # Examples //! //! For some inspiration, see the examples: //! //! * [Monte Carlo estimation of π]( //! https://github.com/rust-lang-nursery/rand/blob/master/examples/monte-carlo.rs) //! * [Monty Hall Problem]( //! https://github.com/rust-lang-nursery/rand/blob/master/examples/monty-hall.rs) //! //! [`Rng`]: trait.Rng.html //! [`Rng::gen()`]: trait.Rng.html#method.gen //! [`RngCore`]: trait.RngCore.html //! [`NewRng`]: trait.NewRng.html //! [`SeedableRng::from_seed`]: trait.SeedableRng.html#tymethod.from_seed //! [`from_rng`]: trait.SeedableRng.html#method.from_rng //! [`CryptoRng`]: trait.CryptoRng.html //! [`thread_rng`]: fn.thread_rng.html //! [`EntropyRng`]: struct.EntropyRng.html //! [`OsRng`]: os/struct.OsRng.html //! [`JitterRng`]: jitter/struct.JitterRng.html //! [`StdRng`]: struct.StdRng.html //! [`SmallRng`]: struct.SmallRng.html //! [`ReseedingRng`]: reseeding/struct.ReseedingRng.html //! [`prng`]: prng/index.html //! [`IsaacRng::new_from_u64`]: struct.IsaacRng.html#method.new_from_u64 //! [`Hc128Rng`]: prng/hc128/struct.Hc128Rng.html //! [`ChaChaRng`]: prng/chacha/struct.ChaChaRng.html //! [`IsaacRng`]: prng/struct.IsaacRng.html //! [`Isaac64Rng`]: prng/struct.Isaac64Rng.html //! [`seq`]: seq/index.html //! [`distributions`]: distributions/index.html //! [`Uniform`]: distributions/struct.Uniform.html #![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/0.5")] #![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_attr(feature = "stdweb", recursion_limit="128")] #[cfg(feature="std")] extern crate std as core; #[cfg(all(feature = "alloc", not(feature="std")))] extern crate alloc; #[cfg(test)] #[cfg(feature="serde-1")] extern crate bincode; #[cfg(feature="serde-1")] extern crate serde; #[cfg(feature="serde-1")] #[macro_use] extern crate serde_derive; #[cfg(feature = "stdweb")] #[macro_use] extern crate stdweb; extern crate rand_core; #[cfg(feature = "log")] #[macro_use] extern crate log; #[cfg(not(feature = "log"))] macro_rules! trace { ($($x:tt)*) => () } #[cfg(not(feature = "log"))] macro_rules! debug { ($($x:tt)*) => () } #[cfg(all(feature="std", not(feature = "log")))] macro_rules! info { ($($x:tt)*) => () } #[cfg(not(feature = "log"))] macro_rules! warn { ($($x:tt)*) => () } #[cfg(all(feature="std", not(feature = "log")))] macro_rules! error { ($($x:tt)*) => () } use core::{marker, mem, slice}; // re-exports from rand_core pub use rand_core::{RngCore, BlockRngCore, CryptoRng, SeedableRng}; pub use rand_core::{ErrorKind, Error}; // external rngs pub use jitter::JitterRng; #[cfg(feature="std")] pub use os::OsRng; // pseudo rngs pub mod prng; pub use isaac::{IsaacRng, Isaac64Rng}; pub use chacha::ChaChaRng; pub use prng::XorShiftRng; pub use prng::Hc128Rng; // convenience and derived rngs #[cfg(feature="std")] pub use entropy_rng::EntropyRng; #[cfg(feature="std")] pub use thread_rng::{ThreadRng, thread_rng}; #[cfg(feature="std")] #[allow(deprecated)] pub use thread_rng::random; use distributions::{Distribution, Uniform, Range}; use distributions::range::SampleRange; // public modules pub mod distributions; pub mod jitter; pub mod mock; #[cfg(feature="std")] pub mod os; #[cfg(feature="std")] pub mod read; pub mod reseeding; #[cfg(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 #[cfg(feature="std")] mod entropy_rng; #[cfg(feature="std")] mod thread_rng; /// A type that can be randomly generated using an `Rng`. /// /// This is merely an adaptor around the [`Uniform`] distribution for /// convenience and backwards-compatibility. /// /// [`Uniform`]: distributions/struct.Uniform.html #[deprecated(since="0.5.0", note="replaced by distributions::Uniform")] 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; } /// An automatically-implemented extension trait on [`RngCore`] providing high-level /// generic methods for sampling values and other convenience methods. /// /// This is the primary trait to use when generating random values. /// /// # Generic usage /// /// The basic pattern is `fn foo<R: Rng + ?Sized>(rng: &mut R)`. Some /// things are worth noting here: /// /// - Since `Rng: RngCore` and every `RngCore` implements `Rng`, it makes no /// difference whether we use `R: Rng` or `R: RngCore`. /// - The `+ ?Sized` un-bounding allows functions to be called directly on /// type-erased references; i.e. `foo(r)` where `r: &mut RngCore`. Without /// this it would be necessary to write `foo(&mut r)`. /// /// An alternative pattern is possible: `fn foo<R: Rng>(rng: R)`. This has some /// trade-offs. It allows the argument to be consumed directly without a `&mut` /// (which is how `from_rng(thread_rng())` works); also it still works directly /// on references (including type-erased references). Unfortunately within the /// function `foo` it is not known whether `rng` is a reference type or not, /// hence many uses of `rng` require an extra reference, either explicitly /// (`distr.sample(&mut rng)`) or implicitly (`rng.gen()`); one may hope the /// optimiser can remove redundant references later. /// /// Example: /// /// ```rust /// use rand::Rng; /// /// fn foo<R: Rng + ?Sized>(rng: &mut R) -> f32 { /// rng.gen() /// } /// ``` /// /// # Iteration /// /// Iteration over an `Rng` can be achieved using `iter::repeat` as follows: /// /// ```rust /// use std::iter; /// use rand::{Rng, thread_rng}; /// use rand::distributions::{Alphanumeric, Range}; /// /// let mut rng = thread_rng(); /// /// // Vec of 16 x f32: /// let v: Vec<f32> = iter::repeat(()).map(|()| rng.gen()).take(16).collect(); /// /// // String: /// let s: String = iter::repeat(()) /// .map(|()| rng.sample(Alphanumeric)) /// .take(7).collect(); /// /// // Dice-rolling: /// let die_range = Range::new_inclusive(1, 6); /// let mut roll_die = iter::repeat(()).map(|()| rng.sample(die_range)); /// while roll_die.next().unwrap() != 6 { /// println!("Not a 6; rolling again!"); /// } /// ``` /// /// [`RngCore`]: https://docs.rs/rand_core/0.1/rand_core/trait.RngCore.html pub trait Rng: RngCore { /// Fill `dest` entirely with random bytes (uniform value distribution), /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.). /// /// On big-endian platforms this performs byte-swapping to ensure /// portability of results from reproducible generators. /// /// This uses [`fill_bytes`] internally which may handle some RNG errors /// implicitly (e.g. waiting if the OS generator is not ready), but panics /// on other errors. See also [`try_fill`] which returns errors. /// /// # Example /// /// ```rust /// use rand::{thread_rng, Rng}; /// /// let mut arr = [0i8; 20]; /// thread_rng().try_fill(&mut arr[..]); /// ``` /// /// [`fill_bytes`]: https://docs.rs/rand_core/0.1/rand_core/trait.RngCore.html#method.fill_bytes /// [`try_fill`]: trait.Rng.html#method.try_fill /// [`AsByteSliceMut`]: trait.AsByteSliceMut.html fn fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) { self.fill_bytes(dest.as_byte_slice_mut()); dest.to_le(); } /// Fill `dest` entirely with random bytes (uniform value distribution), /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.). /// /// On big-endian platforms this performs byte-swapping to ensure /// portability of results from reproducible generators. /// /// This uses [`try_fill_bytes`] internally and forwards all RNG errors. In /// some cases errors may be resolvable; see [`ErrorKind`] and /// documentation for the RNG in use. If you do not plan to handle these /// errors you may prefer to use [`fill`]. /// /// # Example /// /// ```rust /// # use rand::Error; /// use rand::{thread_rng, Rng}; /// /// # fn try_inner() -> Result<(), Error> { /// let mut arr = [0u64; 4]; /// thread_rng().try_fill(&mut arr[..])?; /// # Ok(()) /// # } /// /// # try_inner().unwrap() /// ``` /// /// [`ErrorKind`]: https://docs.rs/rand_core/0.1/rand_core/enum.ErrorKind.html /// [`try_fill_bytes`]: https://docs.rs/rand_core/0.1/rand_core/trait.RngCore.html#method.try_fill_bytes /// [`fill`]: trait.Rng.html#method.fill /// [`AsByteSliceMut`]: trait.AsByteSliceMut.html fn try_fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) -> Result<(), Error> { self.try_fill_bytes(dest.as_byte_slice_mut())?; dest.to_le(); Ok(()) } /// Sample a new value, using the given distribution. /// /// ### Example /// /// ```rust /// use rand::{thread_rng, Rng}; /// use rand::distributions::Range; /// /// let mut rng = thread_rng(); /// let x: i32 = rng.sample(Range::new(10, 15)); /// ``` fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T { distr.sample(self) } /// Return a random value supporting the [`Uniform`] distribution. /// /// [`Uniform`]: struct.Uniform.html /// /// # 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>(&mut self) -> T where Uniform: Distribution<T> { Uniform.sample(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)>>()); /// ``` #[allow(deprecated)] #[deprecated(since="0.5.0", note="use iter::repeat instead")] fn gen_iter<T>(&mut self) -> Generator<T, &mut Self> where Uniform: Distribution<T> { Generator { rng: self, _marker: marker::PhantomData } } /// Generate a random value in the range [`low`, `high`), i.e. inclusive of /// `low` and exclusive of `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 when constructing the `Range`. /// /// # 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 { Range::sample_single(low, high, self) } /// Return a bool with a 1 in n chance of true /// /// # Example /// /// ```rust /// #[allow(deprecated)] /// use rand::{thread_rng, Rng}; /// /// let mut rng = thread_rng(); /// assert_eq!(rng.gen_weighted_bool(0), true); /// assert_eq!(rng.gen_weighted_bool(1), true); /// // Just like `rng.gen::<bool>()` a 50-50% chance, but using a slower /// // method with different results. /// println!("{}", rng.gen_weighted_bool(2)); /// // First meaningful use of `gen_weighted_bool`. /// println!("{}", rng.gen_weighted_bool(3)); /// ``` #[deprecated(since="0.5.0", note="use gen_bool instead")] fn gen_weighted_bool(&mut self, n: u32) -> bool { // Short-circuit after `n <= 1` to avoid panic in `gen_range` n <= 1 || self.gen_range(0, n) == 0 } /// Return a bool with a probability `p` of being true. /// /// # Example /// /// ```rust /// use rand::{thread_rng, Rng}; /// /// let mut rng = thread_rng(); /// println!("{}", rng.gen_bool(1.0 / 3.0)); /// ``` fn gen_bool(&mut self, p: f64) -> bool { assert!(p >= 0.0 && p <= 1.0); // If `p` is constant, this will be evaluated at compile-time. let p_int = (p * core::u32::MAX as f64) as u32; self.gen::<u32>() <= p_int } /// Return an iterator of random characters from the set A-Z,a-z,0-9. /// /// # Example /// /// ```rust /// #[allow(deprecated)] /// use rand::{thread_rng, Rng}; /// /// let s: String = thread_rng().gen_ascii_chars().take(10).collect(); /// println!("{}", s); /// ``` #[allow(deprecated)] #[deprecated(since="0.5.0", note="use distributions::Alphanumeric instead")] fn gen_ascii_chars(&mut self) -> AsciiGenerator<&mut Self> { 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> { 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> { 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]) { 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<R: RngCore + ?Sized> Rng for R {} /// Trait for casting types to byte slices /// /// This is used by the [`fill`] and [`try_fill`] methods. /// /// [`fill`]: trait.Rng.html#method.fill /// [`try_fill`]: trait.Rng.html#method.try_fill pub trait AsByteSliceMut { /// Return a mutable reference to self as a byte slice fn as_byte_slice_mut<'a>(&'a mut self) -> &'a mut [u8]; /// Call `to_le` on each element (i.e. byte-swap on Big Endian platforms). fn to_le(&mut self); } impl AsByteSliceMut for [u8] { fn as_byte_slice_mut<'a>(&'a mut self) -> &'a mut [u8] { self } fn to_le(&mut self) {} } macro_rules! impl_as_byte_slice { ($t:ty) => { impl AsByteSliceMut for [$t] { fn as_byte_slice_mut<'a>(&'a mut self) -> &'a mut [u8] { unsafe { slice::from_raw_parts_mut(&mut self[0] as *mut $t as *mut u8, self.len() * mem::size_of::<$t>() ) } } fn to_le(&mut self) { for x in self { *x = x.to_le(); } } } } } impl_as_byte_slice!(u16); impl_as_byte_slice!(u32); impl_as_byte_slice!(u64); #[cfg(feature="i128_support")] impl_as_byte_slice!(u128); impl_as_byte_slice!(usize); impl_as_byte_slice!(i8); impl_as_byte_slice!(i16); impl_as_byte_slice!(i32); impl_as_byte_slice!(i64); #[cfg(feature="i128_support")] impl_as_byte_slice!(i128); impl_as_byte_slice!(isize); macro_rules! impl_as_byte_slice_arrays { ($n:expr,) => {}; ($n:expr, $N:ident, $($NN:ident,)*) => { impl_as_byte_slice_arrays!($n - 1, $($NN,)*); impl<T> AsByteSliceMut for [T; $n] where [T]: AsByteSliceMut { fn as_byte_slice_mut<'a>(&'a mut self) -> &'a mut [u8] { self[..].as_byte_slice_mut() } fn to_le(&mut self) { self[..].to_le() } } }; } impl_as_byte_slice_arrays!(32, N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,); /// 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)] #[allow(deprecated)] #[deprecated(since="0.5.0", note="use iter::repeat instead")] pub struct Generator<T, R: RngCore> { rng: R, _marker: marker::PhantomData<fn() -> T>, } #[allow(deprecated)] impl<T, R: RngCore> Iterator for Generator<T, R> where Uniform: Distribution<T> { 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)] #[allow(deprecated)] #[deprecated(since="0.5.0", note="use distributions::Alphanumeric instead")] pub struct AsciiGenerator<R: RngCore> { rng: R, } #[allow(deprecated)] impl<R: RngCore> Iterator for AsciiGenerator<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 convenient way to seed new algorithmic generators with fresh entropy from /// `EntropyRng`. /// /// This is the recommended way to create PRNGs, unless a deterministic seed is /// desired (in which case [`SeedableRng::from_seed`] should be used). /// /// Note: this trait is automatically implemented for any PRNG implementing /// [`SeedableRng`] and is not intended to be implemented by users. /// /// ## Example /// /// ``` /// use rand::{StdRng, Rng, NewRng}; /// /// let mut rng = StdRng::new(); /// println!("Random die roll: {}", rng.gen_range(1, 7)); /// ``` /// /// [`SeedableRng`]: https://docs.rs/rand_core/0.1/rand_core/trait.SeedableRng.html /// [`SeedableRng::from_seed`]: https://docs.rs/rand_core/0.1/rand_core/trait.SeedableRng.html#tymethod.from_seed #[cfg(feature="std")] pub trait NewRng: SeedableRng { /// Creates a new instance, automatically seeded with fresh entropy. /// /// Normally this will use `OsRng`, but if that fails `JitterRng` will be /// used instead. Both should be suitable for cryptography. It is possible /// that both entropy sources will fail though unlikely; failures would /// almost certainly be platform limitations or build issues, i.e. most /// applications targetting PC/mobile platforms should not need to worry /// about this failing. /// /// If all entropy sources fail this will panic. If you need to handle /// errors, use the following code, equivalent aside from error handling: /// /// ```rust /// use rand::{Rng, StdRng, EntropyRng, SeedableRng, Error}; /// /// fn foo() -> Result<(), Error> { /// // This uses StdRng, but is valid for any R: SeedableRng /// let mut rng = StdRng::from_rng(EntropyRng::new())?; /// /// println!("random number: {}", rng.gen_range(1, 10)); /// Ok(()) /// } /// ``` fn new() -> Self; } #[cfg(feature="std")] impl<R: SeedableRng> NewRng for R { fn new() -> R { R::from_rng(EntropyRng::new()).unwrap_or_else(|err| panic!("NewRng::new() failed: {}", err)) } } /// The standard RNG. The PRNG algorithm in `StdRng` is chosen to be efficient /// on the current platform, to be statistically strong and unpredictable /// (meaning a cryptographically secure PRNG). /// /// The current algorithm used on all platforms is [HC-128]. /// /// Reproducibility of output from this generator is however not required, thus /// future library versions may use a different internal generator with /// different output. Further, this generator may not be portable and can /// produce different output depending on the architecture. If you require /// reproducible output, use a named RNG, for example `ChaChaRng`. /// /// [HC-128]: prng/hc128/struct.Hc128Rng.html #[derive(Clone, Debug)] pub struct StdRng(Hc128Rng); impl RngCore for StdRng { #[inline(always)] fn next_u32(&mut self) -> u32 { self.0.next_u32() } #[inline(always)] fn next_u64(&mut self) -> u64 { self.0.next_u64() } fn fill_bytes(&mut self, dest: &mut [u8]) { self.0.fill_bytes(dest); } fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { self.0.try_fill_bytes(dest) } } impl SeedableRng for StdRng { type Seed = <Hc128Rng as SeedableRng>::Seed; fn from_seed(seed: Self::Seed) -> Self { StdRng(Hc128Rng::from_seed(seed)) } fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> { Hc128Rng::from_rng(rng).map(|result| StdRng(result)) } } impl CryptoRng for StdRng {} /// An RNG recommended when small state, cheap initialization and good /// performance are required. The PRNG algorithm in `SmallRng` is chosen to be /// efficient on the current platform, **without consideration for cryptography /// or security**. The size of its state is much smaller than for `StdRng`. /// /// Reproducibility of output from this generator is however not required, thus /// future library versions may use a different internal generator with /// different output. Further, this generator may not be portable and can /// produce different output depending on the architecture. If you require /// reproducible output, use a named RNG, for example `XorShiftRng`. /// /// The current algorithm used on all platforms is [Xorshift]. /// /// # Examples /// /// Initializing `StdRng` with a random seed can be done using `NewRng`: /// /// ``` /// use rand::{NewRng, SmallRng}; /// /// // Create small, cheap to initialize and fast RNG with a random seed. /// // The randomness is supplied by the operating system. /// let mut small_rng = SmallRng::new(); /// ``` /// /// When initializing a lot of `SmallRng`, using `thread_rng` can be more /// efficient: /// /// ``` /// use std::iter; /// use rand::{SeedableRng, SmallRng, thread_rng}; /// /// // Create a big, expensive to initialize and slower, but unpredictable RNG. /// // This is cached and done only once per thread. /// let mut thread_rng = thread_rng(); /// // Create small, cheap to initialize and fast RNGs with random seeds. /// // One can generally assume this won't fail. /// let rngs: Vec<SmallRng> = iter::repeat(()) /// .map(|()| SmallRng::from_rng(&mut thread_rng).unwrap()) /// .take(10) /// .collect(); /// ``` /// /// [Xorshift]: struct.XorShiftRng.html #[derive(Clone, Debug)] pub struct SmallRng(XorShiftRng); impl RngCore for SmallRng { #[inline(always)] fn next_u32(&mut self) -> u32 { self.0.next_u32() } #[inline(always)] fn next_u64(&mut self) -> u64 { self.0.next_u64() } fn fill_bytes(&mut self, dest: &mut [u8]) { self.0.fill_bytes(dest); } fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { self.0.try_fill_bytes(dest) } } impl SeedableRng for SmallRng { type Seed = <XorShiftRng as SeedableRng>::Seed; fn from_seed(seed: Self::Seed) -> Self { SmallRng(XorShiftRng::from_seed(seed)) } fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> { XorShiftRng::from_rng(rng).map(|result| SmallRng(result)) } } /// DEPRECATED: use `SmallRng` instead. /// /// 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. #[deprecated(since="0.5.0", note="removed in favor of SmallRng")] #[cfg(feature="std")] pub fn weak_rng() -> XorShiftRng { XorShiftRng::from_rng(thread_rng()).unwrap_or_else(|err| panic!("weak_rng failed: {:?}", err)) } /// 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 mock::StepRng; use super::*; #[cfg(all(not(feature="std"), feature="alloc"))] use alloc::boxed::Box; pub struct TestRng<R> { inner: R } impl<R: RngCore> RngCore for TestRng<R> { fn next_u32(&mut self) -> u32 { self.inner.next_u32() } fn next_u64(&mut self) -> u64 { self.inner.next_u64() } fn fill_bytes(&mut self, dest: &mut [u8]) { self.inner.fill_bytes(dest) } fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { self.inner.try_fill_bytes(dest) } } pub fn rng(seed: u64) -> TestRng<StdRng> { // TODO: use from_hashable let mut state = seed; let mut seed = <StdRng as SeedableRng>::Seed::default(); for x in seed.iter_mut() { // PCG algorithm const MUL: u64 = 6364136223846793005; const INC: u64 = 11634580027462260723; let oldstate = state; state = oldstate.wrapping_mul(MUL).wrapping_add(INC); let xorshifted = (((oldstate >> 18) ^ oldstate) >> 27) as u32; let rot = (oldstate >> 59) as u32; *x = xorshifted.rotate_right(rot) as u8; } TestRng { inner: StdRng::from_seed(seed) } } #[test] fn test_fill_bytes_default() { let mut r = StepRng::new(0x11_22_33_44_55_66_77_88, 0); // 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 buffer = [0u8; 87]; let v = &mut buffer[0..n]; r.fill_bytes(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_fill() { let x = 9041086907909331047; // a random u64 let mut rng = StepRng::new(x, 0); // Convert to byte sequence and back to u64; byte-swap twice if BE. let mut array = [0u64; 2]; rng.fill(&mut array[..]); assert_eq!(array, [x, x]); assert_eq!(rng.next_u64(), x); // Convert to bytes then u32 in LE order let mut array = [0u32; 2]; rng.fill(&mut array[..]); assert_eq!(array, [x as u32, (x >> 32) as u32]); assert_eq!(rng.next_u32(), x as u32); } #[test] fn test_gen_range() { let mut r = rng(101); 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 = rng(102); r.gen_range(5, -2); } #[test] #[should_panic] fn test_gen_range_panic_usize() { let mut r = rng(103); r.gen_range(5, 2); } #[test] #[allow(deprecated)] fn test_gen_weighted_bool() { let mut r = rng(104); assert_eq!(r.gen_weighted_bool(0), true); assert_eq!(r.gen_weighted_bool(1), true); } #[test] fn test_gen_bool() { let mut r = rng(105); for _ in 0..5 { assert_eq!(r.gen_bool(0.0), false); assert_eq!(r.gen_bool(1.0), true); } } #[test] fn test_choose() { let mut r = rng(107); 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 = rng(108); 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_rng_trait_object() { use distributions::{Distribution, Uniform}; let mut rng = rng(109); let mut r = &mut rng as &mut RngCore; 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); let _c: u8 = Uniform.sample(&mut r); } #[test] #[cfg(feature="alloc")] fn test_rng_boxed_trait() { use distributions::{Distribution, Uniform}; let rng = rng(110); let mut r = Box::new(rng) as Box<RngCore>; 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); let _c: u8 = Uniform.sample(&mut r); } #[test] fn test_stdrng_construction() { let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; let mut rng1 = StdRng::from_seed(seed); assert_eq!(rng1.next_u64(), 15759097995037006553); let mut rng2 = StdRng::from_rng(rng1).unwrap(); assert_eq!(rng2.next_u64(), 6766915756997287454); } }