Joel Galenson | 66036a8 | 2020-07-07 13:29:38 -0700 | [diff] [blame] | 1 | // Copyright 2018 Developers of the Rand project. |
| 2 | // Copyright 2013-2017 The Rust Project Developers. |
| 3 | // |
| 4 | // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or |
| 5 | // https://www.apache.org/licenses/LICENSE-2.0> or the MIT license |
| 6 | // <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your |
| 7 | // option. This file may not be copied, modified, or distributed |
| 8 | // except according to those terms. |
| 9 | |
| 10 | //! Utilities for random number generation |
| 11 | //! |
| 12 | //! Rand provides utilities to generate random numbers, to convert them to |
| 13 | //! useful types and distributions, and some randomness-related algorithms. |
| 14 | //! |
| 15 | //! # Quick Start |
| 16 | //! |
| 17 | //! To get you started quickly, the easiest and highest-level way to get |
| 18 | //! a random value is to use [`random()`]; alternatively you can use |
| 19 | //! [`thread_rng()`]. The [`Rng`] trait provides a useful API on all RNGs, while |
| 20 | //! the [`distributions`] and [`seq`] modules provide further |
| 21 | //! functionality on top of RNGs. |
| 22 | //! |
| 23 | //! ``` |
| 24 | //! use rand::prelude::*; |
| 25 | //! |
| 26 | //! if rand::random() { // generates a boolean |
| 27 | //! // Try printing a random unicode code point (probably a bad idea)! |
| 28 | //! println!("char: {}", rand::random::<char>()); |
| 29 | //! } |
| 30 | //! |
| 31 | //! let mut rng = rand::thread_rng(); |
| 32 | //! let y: f64 = rng.gen(); // generates a float between 0 and 1 |
| 33 | //! |
| 34 | //! let mut nums: Vec<i32> = (1..100).collect(); |
| 35 | //! nums.shuffle(&mut rng); |
| 36 | //! ``` |
| 37 | //! |
| 38 | //! # The Book |
| 39 | //! |
| 40 | //! For the user guide and futher documentation, please read |
| 41 | //! [The Rust Rand Book](https://rust-random.github.io/book). |
| 42 | |
| 43 | #![doc( |
| 44 | html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png", |
| 45 | html_favicon_url = "https://www.rust-lang.org/favicon.ico", |
| 46 | html_root_url = "https://rust-random.github.io/rand/" |
| 47 | )] |
| 48 | #![deny(missing_docs)] |
| 49 | #![deny(missing_debug_implementations)] |
| 50 | #![doc(test(attr(allow(unused_variables), deny(warnings))))] |
| 51 | #![cfg_attr(not(feature = "std"), no_std)] |
| 52 | #![cfg_attr(all(feature = "simd_support", feature = "nightly"), feature(stdsimd))] |
| 53 | #![allow( |
| 54 | clippy::excessive_precision, |
| 55 | clippy::unreadable_literal, |
| 56 | clippy::float_cmp |
| 57 | )] |
| 58 | |
| 59 | #[cfg(all(feature = "alloc", not(feature = "std")))] extern crate alloc; |
| 60 | |
| 61 | #[allow(unused)] |
| 62 | macro_rules! trace { ($($x:tt)*) => ( |
| 63 | #[cfg(feature = "log")] { |
| 64 | log::trace!($($x)*) |
| 65 | } |
| 66 | ) } |
| 67 | #[allow(unused)] |
| 68 | macro_rules! debug { ($($x:tt)*) => ( |
| 69 | #[cfg(feature = "log")] { |
| 70 | log::debug!($($x)*) |
| 71 | } |
| 72 | ) } |
| 73 | #[allow(unused)] |
| 74 | macro_rules! info { ($($x:tt)*) => ( |
| 75 | #[cfg(feature = "log")] { |
| 76 | log::info!($($x)*) |
| 77 | } |
| 78 | ) } |
| 79 | #[allow(unused)] |
| 80 | macro_rules! warn { ($($x:tt)*) => ( |
| 81 | #[cfg(feature = "log")] { |
| 82 | log::warn!($($x)*) |
| 83 | } |
| 84 | ) } |
| 85 | #[allow(unused)] |
| 86 | macro_rules! error { ($($x:tt)*) => ( |
| 87 | #[cfg(feature = "log")] { |
| 88 | log::error!($($x)*) |
| 89 | } |
| 90 | ) } |
| 91 | |
| 92 | // Re-exports from rand_core |
| 93 | pub use rand_core::{CryptoRng, Error, RngCore, SeedableRng}; |
| 94 | |
| 95 | // Public exports |
| 96 | #[cfg(feature = "std")] pub use crate::rngs::thread::thread_rng; |
| 97 | |
| 98 | // Public modules |
| 99 | pub mod distributions; |
| 100 | pub mod prelude; |
| 101 | pub mod rngs; |
| 102 | pub mod seq; |
| 103 | |
| 104 | |
| 105 | use crate::distributions::uniform::{SampleBorrow, SampleUniform, UniformSampler}; |
| 106 | use crate::distributions::{Distribution, Standard}; |
| 107 | use core::num::Wrapping; |
| 108 | use core::{mem, slice}; |
| 109 | |
| 110 | /// An automatically-implemented extension trait on [`RngCore`] providing high-level |
| 111 | /// generic methods for sampling values and other convenience methods. |
| 112 | /// |
| 113 | /// This is the primary trait to use when generating random values. |
| 114 | /// |
| 115 | /// # Generic usage |
| 116 | /// |
| 117 | /// The basic pattern is `fn foo<R: Rng + ?Sized>(rng: &mut R)`. Some |
| 118 | /// things are worth noting here: |
| 119 | /// |
| 120 | /// - Since `Rng: RngCore` and every `RngCore` implements `Rng`, it makes no |
| 121 | /// difference whether we use `R: Rng` or `R: RngCore`. |
| 122 | /// - The `+ ?Sized` un-bounding allows functions to be called directly on |
| 123 | /// type-erased references; i.e. `foo(r)` where `r: &mut RngCore`. Without |
| 124 | /// this it would be necessary to write `foo(&mut r)`. |
| 125 | /// |
| 126 | /// An alternative pattern is possible: `fn foo<R: Rng>(rng: R)`. This has some |
| 127 | /// trade-offs. It allows the argument to be consumed directly without a `&mut` |
| 128 | /// (which is how `from_rng(thread_rng())` works); also it still works directly |
| 129 | /// on references (including type-erased references). Unfortunately within the |
| 130 | /// function `foo` it is not known whether `rng` is a reference type or not, |
| 131 | /// hence many uses of `rng` require an extra reference, either explicitly |
| 132 | /// (`distr.sample(&mut rng)`) or implicitly (`rng.gen()`); one may hope the |
| 133 | /// optimiser can remove redundant references later. |
| 134 | /// |
| 135 | /// Example: |
| 136 | /// |
| 137 | /// ``` |
| 138 | /// # use rand::thread_rng; |
| 139 | /// use rand::Rng; |
| 140 | /// |
| 141 | /// fn foo<R: Rng + ?Sized>(rng: &mut R) -> f32 { |
| 142 | /// rng.gen() |
| 143 | /// } |
| 144 | /// |
| 145 | /// # let v = foo(&mut thread_rng()); |
| 146 | /// ``` |
| 147 | pub trait Rng: RngCore { |
| 148 | /// Return a random value supporting the [`Standard`] distribution. |
| 149 | /// |
| 150 | /// # Example |
| 151 | /// |
| 152 | /// ``` |
| 153 | /// use rand::{thread_rng, Rng}; |
| 154 | /// |
| 155 | /// let mut rng = thread_rng(); |
| 156 | /// let x: u32 = rng.gen(); |
| 157 | /// println!("{}", x); |
| 158 | /// println!("{:?}", rng.gen::<(f64, bool)>()); |
| 159 | /// ``` |
| 160 | /// |
| 161 | /// # Arrays and tuples |
| 162 | /// |
| 163 | /// The `rng.gen()` method is able to generate arrays (up to 32 elements) |
| 164 | /// and tuples (up to 12 elements), so long as all element types can be |
| 165 | /// generated. |
| 166 | /// |
| 167 | /// For arrays of integers, especially for those with small element types |
| 168 | /// (< 64 bit), it will likely be faster to instead use [`Rng::fill`]. |
| 169 | /// |
| 170 | /// ``` |
| 171 | /// use rand::{thread_rng, Rng}; |
| 172 | /// |
| 173 | /// let mut rng = thread_rng(); |
| 174 | /// let tuple: (u8, i32, char) = rng.gen(); // arbitrary tuple support |
| 175 | /// |
| 176 | /// let arr1: [f32; 32] = rng.gen(); // array construction |
| 177 | /// let mut arr2 = [0u8; 128]; |
| 178 | /// rng.fill(&mut arr2); // array fill |
| 179 | /// ``` |
| 180 | /// |
| 181 | /// [`Standard`]: distributions::Standard |
| 182 | #[inline] |
| 183 | fn gen<T>(&mut self) -> T |
| 184 | where Standard: Distribution<T> { |
| 185 | Standard.sample(self) |
| 186 | } |
| 187 | |
| 188 | /// Generate a random value in the range [`low`, `high`), i.e. inclusive of |
| 189 | /// `low` and exclusive of `high`. |
| 190 | /// |
| 191 | /// This function is optimised for the case that only a single sample is |
| 192 | /// made from the given range. See also the [`Uniform`] distribution |
| 193 | /// type which may be faster if sampling from the same range repeatedly. |
| 194 | /// |
| 195 | /// # Panics |
| 196 | /// |
| 197 | /// Panics if `low >= high`. |
| 198 | /// |
| 199 | /// # Example |
| 200 | /// |
| 201 | /// ``` |
| 202 | /// use rand::{thread_rng, Rng}; |
| 203 | /// |
| 204 | /// let mut rng = thread_rng(); |
| 205 | /// let n: u32 = rng.gen_range(0, 10); |
| 206 | /// println!("{}", n); |
| 207 | /// let m: f64 = rng.gen_range(-40.0f64, 1.3e5f64); |
| 208 | /// println!("{}", m); |
| 209 | /// ``` |
| 210 | /// |
| 211 | /// [`Uniform`]: distributions::uniform::Uniform |
| 212 | fn gen_range<T: SampleUniform, B1, B2>(&mut self, low: B1, high: B2) -> T |
| 213 | where |
| 214 | B1: SampleBorrow<T> + Sized, |
| 215 | B2: SampleBorrow<T> + Sized, |
| 216 | { |
| 217 | T::Sampler::sample_single(low, high, self) |
| 218 | } |
| 219 | |
| 220 | /// Sample a new value, using the given distribution. |
| 221 | /// |
| 222 | /// ### Example |
| 223 | /// |
| 224 | /// ``` |
| 225 | /// use rand::{thread_rng, Rng}; |
| 226 | /// use rand::distributions::Uniform; |
| 227 | /// |
| 228 | /// let mut rng = thread_rng(); |
| 229 | /// let x = rng.sample(Uniform::new(10u32, 15)); |
| 230 | /// // Type annotation requires two types, the type and distribution; the |
| 231 | /// // distribution can be inferred. |
| 232 | /// let y = rng.sample::<u16, _>(Uniform::new(10, 15)); |
| 233 | /// ``` |
| 234 | fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T { |
| 235 | distr.sample(self) |
| 236 | } |
| 237 | |
| 238 | /// Create an iterator that generates values using the given distribution. |
| 239 | /// |
| 240 | /// Note that this function takes its arguments by value. This works since |
| 241 | /// `(&mut R): Rng where R: Rng` and |
| 242 | /// `(&D): Distribution where D: Distribution`, |
| 243 | /// however borrowing is not automatic hence `rng.sample_iter(...)` may |
| 244 | /// need to be replaced with `(&mut rng).sample_iter(...)`. |
| 245 | /// |
| 246 | /// # Example |
| 247 | /// |
| 248 | /// ``` |
| 249 | /// use rand::{thread_rng, Rng}; |
| 250 | /// use rand::distributions::{Alphanumeric, Uniform, Standard}; |
| 251 | /// |
| 252 | /// let rng = thread_rng(); |
| 253 | /// |
| 254 | /// // Vec of 16 x f32: |
| 255 | /// let v: Vec<f32> = rng.sample_iter(Standard).take(16).collect(); |
| 256 | /// |
| 257 | /// // String: |
| 258 | /// let s: String = rng.sample_iter(Alphanumeric).take(7).collect(); |
| 259 | /// |
| 260 | /// // Combined values |
| 261 | /// println!("{:?}", rng.sample_iter(Standard).take(5) |
| 262 | /// .collect::<Vec<(f64, bool)>>()); |
| 263 | /// |
| 264 | /// // Dice-rolling: |
| 265 | /// let die_range = Uniform::new_inclusive(1, 6); |
| 266 | /// let mut roll_die = rng.sample_iter(die_range); |
| 267 | /// while roll_die.next().unwrap() != 6 { |
| 268 | /// println!("Not a 6; rolling again!"); |
| 269 | /// } |
| 270 | /// ``` |
| 271 | fn sample_iter<T, D>(self, distr: D) -> distributions::DistIter<D, Self, T> |
| 272 | where |
| 273 | D: Distribution<T>, |
| 274 | Self: Sized, |
| 275 | { |
| 276 | distr.sample_iter(self) |
| 277 | } |
| 278 | |
| 279 | /// Fill `dest` entirely with random bytes (uniform value distribution), |
| 280 | /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices |
| 281 | /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.). |
| 282 | /// |
| 283 | /// On big-endian platforms this performs byte-swapping to ensure |
| 284 | /// portability of results from reproducible generators. |
| 285 | /// |
| 286 | /// This uses [`fill_bytes`] internally which may handle some RNG errors |
| 287 | /// implicitly (e.g. waiting if the OS generator is not ready), but panics |
| 288 | /// on other errors. See also [`try_fill`] which returns errors. |
| 289 | /// |
| 290 | /// # Example |
| 291 | /// |
| 292 | /// ``` |
| 293 | /// use rand::{thread_rng, Rng}; |
| 294 | /// |
| 295 | /// let mut arr = [0i8; 20]; |
| 296 | /// thread_rng().fill(&mut arr[..]); |
| 297 | /// ``` |
| 298 | /// |
| 299 | /// [`fill_bytes`]: RngCore::fill_bytes |
| 300 | /// [`try_fill`]: Rng::try_fill |
| 301 | fn fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) { |
| 302 | self.fill_bytes(dest.as_byte_slice_mut()); |
| 303 | dest.to_le(); |
| 304 | } |
| 305 | |
| 306 | /// Fill `dest` entirely with random bytes (uniform value distribution), |
| 307 | /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices |
| 308 | /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.). |
| 309 | /// |
| 310 | /// On big-endian platforms this performs byte-swapping to ensure |
| 311 | /// portability of results from reproducible generators. |
| 312 | /// |
| 313 | /// This is identical to [`fill`] except that it uses [`try_fill_bytes`] |
| 314 | /// internally and forwards RNG errors. |
| 315 | /// |
| 316 | /// # Example |
| 317 | /// |
| 318 | /// ``` |
| 319 | /// # use rand::Error; |
| 320 | /// use rand::{thread_rng, Rng}; |
| 321 | /// |
| 322 | /// # fn try_inner() -> Result<(), Error> { |
| 323 | /// let mut arr = [0u64; 4]; |
| 324 | /// thread_rng().try_fill(&mut arr[..])?; |
| 325 | /// # Ok(()) |
| 326 | /// # } |
| 327 | /// |
| 328 | /// # try_inner().unwrap() |
| 329 | /// ``` |
| 330 | /// |
| 331 | /// [`try_fill_bytes`]: RngCore::try_fill_bytes |
| 332 | /// [`fill`]: Rng::fill |
| 333 | fn try_fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) -> Result<(), Error> { |
| 334 | self.try_fill_bytes(dest.as_byte_slice_mut())?; |
| 335 | dest.to_le(); |
| 336 | Ok(()) |
| 337 | } |
| 338 | |
| 339 | /// Return a bool with a probability `p` of being true. |
| 340 | /// |
| 341 | /// See also the [`Bernoulli`] distribution, which may be faster if |
| 342 | /// sampling from the same probability repeatedly. |
| 343 | /// |
| 344 | /// # Example |
| 345 | /// |
| 346 | /// ``` |
| 347 | /// use rand::{thread_rng, Rng}; |
| 348 | /// |
| 349 | /// let mut rng = thread_rng(); |
| 350 | /// println!("{}", rng.gen_bool(1.0 / 3.0)); |
| 351 | /// ``` |
| 352 | /// |
| 353 | /// # Panics |
| 354 | /// |
| 355 | /// If `p < 0` or `p > 1`. |
| 356 | /// |
| 357 | /// [`Bernoulli`]: distributions::bernoulli::Bernoulli |
| 358 | #[inline] |
| 359 | fn gen_bool(&mut self, p: f64) -> bool { |
| 360 | let d = distributions::Bernoulli::new(p).unwrap(); |
| 361 | self.sample(d) |
| 362 | } |
| 363 | |
| 364 | /// Return a bool with a probability of `numerator/denominator` of being |
| 365 | /// true. I.e. `gen_ratio(2, 3)` has chance of 2 in 3, or about 67%, of |
| 366 | /// returning true. If `numerator == denominator`, then the returned value |
| 367 | /// is guaranteed to be `true`. If `numerator == 0`, then the returned |
| 368 | /// value is guaranteed to be `false`. |
| 369 | /// |
| 370 | /// See also the [`Bernoulli`] distribution, which may be faster if |
| 371 | /// sampling from the same `numerator` and `denominator` repeatedly. |
| 372 | /// |
| 373 | /// # Panics |
| 374 | /// |
| 375 | /// If `denominator == 0` or `numerator > denominator`. |
| 376 | /// |
| 377 | /// # Example |
| 378 | /// |
| 379 | /// ``` |
| 380 | /// use rand::{thread_rng, Rng}; |
| 381 | /// |
| 382 | /// let mut rng = thread_rng(); |
| 383 | /// println!("{}", rng.gen_ratio(2, 3)); |
| 384 | /// ``` |
| 385 | /// |
| 386 | /// [`Bernoulli`]: distributions::bernoulli::Bernoulli |
| 387 | #[inline] |
| 388 | fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool { |
| 389 | let d = distributions::Bernoulli::from_ratio(numerator, denominator).unwrap(); |
| 390 | self.sample(d) |
| 391 | } |
| 392 | } |
| 393 | |
| 394 | impl<R: RngCore + ?Sized> Rng for R {} |
| 395 | |
| 396 | /// Trait for casting types to byte slices |
| 397 | /// |
| 398 | /// This is used by the [`Rng::fill`] and [`Rng::try_fill`] methods. |
| 399 | pub trait AsByteSliceMut { |
| 400 | /// Return a mutable reference to self as a byte slice |
| 401 | fn as_byte_slice_mut(&mut self) -> &mut [u8]; |
| 402 | |
| 403 | /// Call `to_le` on each element (i.e. byte-swap on Big Endian platforms). |
| 404 | fn to_le(&mut self); |
| 405 | } |
| 406 | |
| 407 | impl AsByteSliceMut for [u8] { |
| 408 | fn as_byte_slice_mut(&mut self) -> &mut [u8] { |
| 409 | self |
| 410 | } |
| 411 | |
| 412 | fn to_le(&mut self) {} |
| 413 | } |
| 414 | |
| 415 | macro_rules! impl_as_byte_slice { |
| 416 | () => {}; |
| 417 | ($t:ty) => { |
| 418 | impl AsByteSliceMut for [$t] { |
| 419 | fn as_byte_slice_mut(&mut self) -> &mut [u8] { |
| 420 | if self.len() == 0 { |
| 421 | unsafe { |
| 422 | // must not use null pointer |
| 423 | slice::from_raw_parts_mut(0x1 as *mut u8, 0) |
| 424 | } |
| 425 | } else { |
| 426 | unsafe { |
| 427 | slice::from_raw_parts_mut(self.as_mut_ptr() |
| 428 | as *mut u8, |
| 429 | self.len() * mem::size_of::<$t>() |
| 430 | ) |
| 431 | } |
| 432 | } |
| 433 | } |
| 434 | |
| 435 | fn to_le(&mut self) { |
| 436 | for x in self { |
| 437 | *x = x.to_le(); |
| 438 | } |
| 439 | } |
| 440 | } |
| 441 | |
| 442 | impl AsByteSliceMut for [Wrapping<$t>] { |
| 443 | fn as_byte_slice_mut(&mut self) -> &mut [u8] { |
| 444 | if self.len() == 0 { |
| 445 | unsafe { |
| 446 | // must not use null pointer |
| 447 | slice::from_raw_parts_mut(0x1 as *mut u8, 0) |
| 448 | } |
| 449 | } else { |
| 450 | unsafe { |
| 451 | slice::from_raw_parts_mut(self.as_mut_ptr() |
| 452 | as *mut u8, |
| 453 | self.len() * mem::size_of::<$t>() |
| 454 | ) |
| 455 | } |
| 456 | } |
| 457 | } |
| 458 | |
| 459 | fn to_le(&mut self) { |
| 460 | for x in self { |
| 461 | *x = Wrapping(x.0.to_le()); |
| 462 | } |
| 463 | } |
| 464 | } |
| 465 | }; |
| 466 | ($t:ty, $($tt:ty,)*) => { |
| 467 | impl_as_byte_slice!($t); |
| 468 | // TODO: this could replace above impl once Rust #32463 is fixed |
| 469 | // impl_as_byte_slice!(Wrapping<$t>); |
| 470 | impl_as_byte_slice!($($tt,)*); |
| 471 | } |
| 472 | } |
| 473 | |
| 474 | impl_as_byte_slice!(u16, u32, u64, usize,); |
| 475 | #[cfg(not(target_os = "emscripten"))] |
| 476 | impl_as_byte_slice!(u128); |
| 477 | impl_as_byte_slice!(i8, i16, i32, i64, isize,); |
| 478 | #[cfg(not(target_os = "emscripten"))] |
| 479 | impl_as_byte_slice!(i128); |
| 480 | |
| 481 | macro_rules! impl_as_byte_slice_arrays { |
| 482 | ($n:expr,) => {}; |
| 483 | ($n:expr, $N:ident) => { |
| 484 | impl<T> AsByteSliceMut for [T; $n] where [T]: AsByteSliceMut { |
| 485 | fn as_byte_slice_mut(&mut self) -> &mut [u8] { |
| 486 | self[..].as_byte_slice_mut() |
| 487 | } |
| 488 | |
| 489 | fn to_le(&mut self) { |
| 490 | self[..].to_le() |
| 491 | } |
| 492 | } |
| 493 | }; |
| 494 | ($n:expr, $N:ident, $($NN:ident,)*) => { |
| 495 | impl_as_byte_slice_arrays!($n, $N); |
| 496 | impl_as_byte_slice_arrays!($n - 1, $($NN,)*); |
| 497 | }; |
| 498 | (!div $n:expr,) => {}; |
| 499 | (!div $n:expr, $N:ident, $($NN:ident,)*) => { |
| 500 | impl_as_byte_slice_arrays!($n, $N); |
| 501 | impl_as_byte_slice_arrays!(!div $n / 2, $($NN,)*); |
| 502 | }; |
| 503 | } |
| 504 | #[rustfmt::skip] |
| 505 | 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,N,); |
| 506 | impl_as_byte_slice_arrays!(!div 4096, N,N,N,N,N,N,N,); |
| 507 | |
| 508 | /// Generates a random value using the thread-local random number generator. |
| 509 | /// |
| 510 | /// This is simply a shortcut for `thread_rng().gen()`. See [`thread_rng`] for |
| 511 | /// documentation of the entropy source and [`Standard`] for documentation of |
| 512 | /// distributions and type-specific generation. |
| 513 | /// |
| 514 | /// # Examples |
| 515 | /// |
| 516 | /// ``` |
| 517 | /// let x = rand::random::<u8>(); |
| 518 | /// println!("{}", x); |
| 519 | /// |
| 520 | /// let y = rand::random::<f64>(); |
| 521 | /// println!("{}", y); |
| 522 | /// |
| 523 | /// if rand::random() { // generates a boolean |
| 524 | /// println!("Better lucky than good!"); |
| 525 | /// } |
| 526 | /// ``` |
| 527 | /// |
| 528 | /// If you're calling `random()` in a loop, caching the generator as in the |
| 529 | /// following example can increase performance. |
| 530 | /// |
| 531 | /// ``` |
| 532 | /// use rand::Rng; |
| 533 | /// |
| 534 | /// let mut v = vec![1, 2, 3]; |
| 535 | /// |
| 536 | /// for x in v.iter_mut() { |
| 537 | /// *x = rand::random() |
| 538 | /// } |
| 539 | /// |
| 540 | /// // can be made faster by caching thread_rng |
| 541 | /// |
| 542 | /// let mut rng = rand::thread_rng(); |
| 543 | /// |
| 544 | /// for x in v.iter_mut() { |
| 545 | /// *x = rng.gen(); |
| 546 | /// } |
| 547 | /// ``` |
| 548 | /// |
| 549 | /// [`Standard`]: distributions::Standard |
| 550 | #[cfg(feature = "std")] |
| 551 | #[inline] |
| 552 | pub fn random<T>() -> T |
| 553 | where Standard: Distribution<T> { |
| 554 | thread_rng().gen() |
| 555 | } |
| 556 | |
| 557 | #[cfg(test)] |
| 558 | mod test { |
| 559 | use super::*; |
| 560 | use crate::rngs::mock::StepRng; |
| 561 | #[cfg(all(not(feature = "std"), feature = "alloc"))] use alloc::boxed::Box; |
| 562 | |
| 563 | /// Construct a deterministic RNG with the given seed |
| 564 | pub fn rng(seed: u64) -> impl RngCore { |
| 565 | // For tests, we want a statistically good, fast, reproducible RNG. |
| 566 | // PCG32 will do fine, and will be easy to embed if we ever need to. |
| 567 | const INC: u64 = 11634580027462260723; |
| 568 | rand_pcg::Pcg32::new(seed, INC) |
| 569 | } |
| 570 | |
| 571 | #[test] |
| 572 | fn test_fill_bytes_default() { |
| 573 | let mut r = StepRng::new(0x11_22_33_44_55_66_77_88, 0); |
| 574 | |
| 575 | // check every remainder mod 8, both in small and big vectors. |
| 576 | let lengths = [0, 1, 2, 3, 4, 5, 6, 7, 80, 81, 82, 83, 84, 85, 86, 87]; |
| 577 | for &n in lengths.iter() { |
| 578 | let mut buffer = [0u8; 87]; |
| 579 | let v = &mut buffer[0..n]; |
| 580 | r.fill_bytes(v); |
| 581 | |
| 582 | // use this to get nicer error messages. |
| 583 | for (i, &byte) in v.iter().enumerate() { |
| 584 | if byte == 0 { |
| 585 | panic!("byte {} of {} is zero", i, n) |
| 586 | } |
| 587 | } |
| 588 | } |
| 589 | } |
| 590 | |
| 591 | #[test] |
| 592 | fn test_fill() { |
| 593 | let x = 9041086907909331047; // a random u64 |
| 594 | let mut rng = StepRng::new(x, 0); |
| 595 | |
| 596 | // Convert to byte sequence and back to u64; byte-swap twice if BE. |
| 597 | let mut array = [0u64; 2]; |
| 598 | rng.fill(&mut array[..]); |
| 599 | assert_eq!(array, [x, x]); |
| 600 | assert_eq!(rng.next_u64(), x); |
| 601 | |
| 602 | // Convert to bytes then u32 in LE order |
| 603 | let mut array = [0u32; 2]; |
| 604 | rng.fill(&mut array[..]); |
| 605 | assert_eq!(array, [x as u32, (x >> 32) as u32]); |
| 606 | assert_eq!(rng.next_u32(), x as u32); |
| 607 | |
| 608 | // Check equivalence using wrapped arrays |
| 609 | let mut warray = [Wrapping(0u32); 2]; |
| 610 | rng.fill(&mut warray[..]); |
| 611 | assert_eq!(array[0], warray[0].0); |
| 612 | assert_eq!(array[1], warray[1].0); |
| 613 | } |
| 614 | |
| 615 | #[test] |
| 616 | fn test_fill_empty() { |
| 617 | let mut array = [0u32; 0]; |
| 618 | let mut rng = StepRng::new(0, 1); |
| 619 | rng.fill(&mut array); |
| 620 | rng.fill(&mut array[..]); |
| 621 | } |
| 622 | |
| 623 | #[test] |
| 624 | fn test_gen_range() { |
| 625 | let mut r = rng(101); |
| 626 | for _ in 0..1000 { |
| 627 | let a = r.gen_range(-4711, 17); |
| 628 | assert!(a >= -4711 && a < 17); |
| 629 | let a = r.gen_range(-3i8, 42); |
| 630 | assert!(a >= -3i8 && a < 42i8); |
| 631 | let a = r.gen_range(&10u16, 99); |
| 632 | assert!(a >= 10u16 && a < 99u16); |
| 633 | let a = r.gen_range(-100i32, &2000); |
| 634 | assert!(a >= -100i32 && a < 2000i32); |
| 635 | let a = r.gen_range(&12u32, &24u32); |
| 636 | assert!(a >= 12u32 && a < 24u32); |
| 637 | |
| 638 | assert_eq!(r.gen_range(0u32, 1), 0u32); |
| 639 | assert_eq!(r.gen_range(-12i64, -11), -12i64); |
| 640 | assert_eq!(r.gen_range(3_000_000, 3_000_001), 3_000_000); |
| 641 | } |
| 642 | } |
| 643 | |
| 644 | #[test] |
| 645 | #[should_panic] |
| 646 | fn test_gen_range_panic_int() { |
| 647 | let mut r = rng(102); |
| 648 | r.gen_range(5, -2); |
| 649 | } |
| 650 | |
| 651 | #[test] |
| 652 | #[should_panic] |
| 653 | fn test_gen_range_panic_usize() { |
| 654 | let mut r = rng(103); |
| 655 | r.gen_range(5, 2); |
| 656 | } |
| 657 | |
| 658 | #[test] |
| 659 | fn test_gen_bool() { |
| 660 | let mut r = rng(105); |
| 661 | for _ in 0..5 { |
| 662 | assert_eq!(r.gen_bool(0.0), false); |
| 663 | assert_eq!(r.gen_bool(1.0), true); |
| 664 | } |
| 665 | } |
| 666 | |
| 667 | #[test] |
| 668 | fn test_rng_trait_object() { |
| 669 | use crate::distributions::{Distribution, Standard}; |
| 670 | let mut rng = rng(109); |
| 671 | let mut r = &mut rng as &mut dyn RngCore; |
| 672 | r.next_u32(); |
| 673 | r.gen::<i32>(); |
| 674 | assert_eq!(r.gen_range(0, 1), 0); |
| 675 | let _c: u8 = Standard.sample(&mut r); |
| 676 | } |
| 677 | |
| 678 | #[test] |
| 679 | #[cfg(feature = "alloc")] |
| 680 | fn test_rng_boxed_trait() { |
| 681 | use crate::distributions::{Distribution, Standard}; |
| 682 | let rng = rng(110); |
| 683 | let mut r = Box::new(rng) as Box<dyn RngCore>; |
| 684 | r.next_u32(); |
| 685 | r.gen::<i32>(); |
| 686 | assert_eq!(r.gen_range(0, 1), 0); |
| 687 | let _c: u8 = Standard.sample(&mut r); |
| 688 | } |
| 689 | |
| 690 | #[test] |
| 691 | #[cfg(feature = "std")] |
| 692 | fn test_random() { |
| 693 | // not sure how to test this aside from just getting some values |
| 694 | let _n: usize = random(); |
| 695 | let _f: f32 = random(); |
| 696 | let _o: Option<Option<i8>> = random(); |
| 697 | let _many: ( |
| 698 | (), |
| 699 | (usize, isize, Option<(u32, (bool,))>), |
| 700 | (u8, i8, u16, i16, u32, i32, u64, i64), |
| 701 | (f32, (f64, (f64,))), |
| 702 | ) = random(); |
| 703 | } |
| 704 | |
| 705 | #[test] |
| 706 | #[cfg_attr(miri, ignore)] // Miri is too slow |
| 707 | fn test_gen_ratio_average() { |
| 708 | const NUM: u32 = 3; |
| 709 | const DENOM: u32 = 10; |
| 710 | const N: u32 = 100_000; |
| 711 | |
| 712 | let mut sum: u32 = 0; |
| 713 | let mut rng = rng(111); |
| 714 | for _ in 0..N { |
| 715 | if rng.gen_ratio(NUM, DENOM) { |
| 716 | sum += 1; |
| 717 | } |
| 718 | } |
| 719 | // Have Binomial(N, NUM/DENOM) distribution |
| 720 | let expected = (NUM * N) / DENOM; // exact integer |
| 721 | assert!(((sum - expected) as i32).abs() < 500); |
| 722 | } |
| 723 | } |