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//! [`Rng`] trait
11
12use crate::distr::uniform::{SampleRange, SampleUniform};
13use crate::distr::{self, Distribution, StandardUniform};
14use core::num::Wrapping;
15use rand_core::RngCore;
16use zerocopy::IntoBytes;
17
18/// User-level interface for RNGs
19///
20/// [`RngCore`] is the `dyn`-safe implementation-level interface for Random
21/// (Number) Generators. This trait, `Rng`, provides a user-level interface on
22/// RNGs. It is implemented automatically for any `R: RngCore`.
23///
24/// This trait must usually be brought into scope via `use rand::Rng;` or
25/// `use rand::prelude::*;`.
26///
27/// # Generic usage
28///
29/// The basic pattern is `fn foo<R: Rng + ?Sized>(rng: &mut R)`. Some
30/// things are worth noting here:
31///
32/// - Since `Rng: RngCore` and every `RngCore` implements `Rng`, it makes no
33/// difference whether we use `R: Rng` or `R: RngCore`.
34/// - The `+ ?Sized` un-bounding allows functions to be called directly on
35/// type-erased references; i.e. `foo(r)` where `r: &mut dyn RngCore`. Without
36/// this it would be necessary to write `foo(&mut r)`.
37///
38/// An alternative pattern is possible: `fn foo<R: Rng>(rng: R)`. This has some
39/// trade-offs. It allows the argument to be consumed directly without a `&mut`
40/// (which is how `from_rng(rand::rng())` works); also it still works directly
41/// on references (including type-erased references). Unfortunately within the
42/// function `foo` it is not known whether `rng` is a reference type or not,
43/// hence many uses of `rng` require an extra reference, either explicitly
44/// (`distr.sample(&mut rng)`) or implicitly (`rng.random()`); one may hope the
45/// optimiser can remove redundant references later.
46///
47/// Example:
48///
49/// ```
50/// use rand::Rng;
51///
52/// fn foo<R: Rng + ?Sized>(rng: &mut R) -> f32 {
53/// rng.random()
54/// }
55///
56/// # let v = foo(&mut rand::rng());
57/// ```
58pub trait Rng: RngCore {
59 /// Return a random value via the [`StandardUniform`] distribution.
60 ///
61 /// # Example
62 ///
63 /// ```
64 /// use rand::Rng;
65 ///
66 /// let mut rng = rand::rng();
67 /// let x: u32 = rng.random();
68 /// println!("{}", x);
69 /// println!("{:?}", rng.random::<(f64, bool)>());
70 /// ```
71 ///
72 /// # Arrays and tuples
73 ///
74 /// The `rng.random()` method is able to generate arrays
75 /// and tuples (up to 12 elements), so long as all element types can be
76 /// generated.
77 ///
78 /// For arrays of integers, especially for those with small element types
79 /// (< 64 bit), it will likely be faster to instead use [`Rng::fill`],
80 /// though note that generated values will differ.
81 ///
82 /// ```
83 /// use rand::Rng;
84 ///
85 /// let mut rng = rand::rng();
86 /// let tuple: (u8, i32, char) = rng.random(); // arbitrary tuple support
87 ///
88 /// let arr1: [f32; 32] = rng.random(); // array construction
89 /// let mut arr2 = [0u8; 128];
90 /// rng.fill(&mut arr2); // array fill
91 /// ```
92 ///
93 /// [`StandardUniform`]: distr::StandardUniform
94 #[inline]
95 fn random<T>(&mut self) -> T
96 where
97 StandardUniform: Distribution<T>,
98 {
99 StandardUniform.sample(self)
100 }
101
102 /// Return an iterator over [`random`](Self::random) variates
103 ///
104 /// This is a just a wrapper over [`Rng::sample_iter`] using
105 /// [`distr::StandardUniform`].
106 ///
107 /// Note: this method consumes its argument. Use
108 /// `(&mut rng).random_iter()` to avoid consuming the RNG.
109 ///
110 /// # Example
111 ///
112 /// ```
113 /// use rand::{rngs::mock::StepRng, Rng};
114 ///
115 /// let rng = StepRng::new(1, 1);
116 /// let v: Vec<i32> = rng.random_iter().take(5).collect();
117 /// assert_eq!(&v, &[1, 2, 3, 4, 5]);
118 /// ```
119 #[inline]
120 fn random_iter<T>(self) -> distr::Iter<StandardUniform, Self, T>
121 where
122 Self: Sized,
123 StandardUniform: Distribution<T>,
124 {
125 StandardUniform.sample_iter(self)
126 }
127
128 /// Generate a random value in the given range.
129 ///
130 /// This function is optimised for the case that only a single sample is
131 /// made from the given range. See also the [`Uniform`] distribution
132 /// type which may be faster if sampling from the same range repeatedly.
133 ///
134 /// All types support `low..high_exclusive` and `low..=high` range syntax.
135 /// Unsigned integer types also support `..high_exclusive` and `..=high` syntax.
136 ///
137 /// # Panics
138 ///
139 /// Panics if the range is empty, or if `high - low` overflows for floats.
140 ///
141 /// # Example
142 ///
143 /// ```
144 /// use rand::Rng;
145 ///
146 /// let mut rng = rand::rng();
147 ///
148 /// // Exclusive range
149 /// let n: u32 = rng.random_range(..10);
150 /// println!("{}", n);
151 /// let m: f64 = rng.random_range(-40.0..1.3e5);
152 /// println!("{}", m);
153 ///
154 /// // Inclusive range
155 /// let n: u32 = rng.random_range(..=10);
156 /// println!("{}", n);
157 /// ```
158 ///
159 /// [`Uniform`]: distr::uniform::Uniform
160 #[track_caller]
161 fn random_range<T, R>(&mut self, range: R) -> T
162 where
163 T: SampleUniform,
164 R: SampleRange<T>,
165 {
166 assert!(!range.is_empty(), "cannot sample empty range");
167 range.sample_single(self).unwrap()
168 }
169
170 /// Return a bool with a probability `p` of being true.
171 ///
172 /// See also the [`Bernoulli`] distribution, which may be faster if
173 /// sampling from the same probability repeatedly.
174 ///
175 /// # Example
176 ///
177 /// ```
178 /// use rand::Rng;
179 ///
180 /// let mut rng = rand::rng();
181 /// println!("{}", rng.random_bool(1.0 / 3.0));
182 /// ```
183 ///
184 /// # Panics
185 ///
186 /// If `p < 0` or `p > 1`.
187 ///
188 /// [`Bernoulli`]: distr::Bernoulli
189 #[inline]
190 #[track_caller]
191 fn random_bool(&mut self, p: f64) -> bool {
192 match distr::Bernoulli::new(p) {
193 Ok(d) => self.sample(d),
194 Err(_) => panic!("p={:?} is outside range [0.0, 1.0]", p),
195 }
196 }
197
198 /// Return a bool with a probability of `numerator/denominator` of being
199 /// true.
200 ///
201 /// That is, `random_ratio(2, 3)` has chance of 2 in 3, or about 67%, of
202 /// returning true. If `numerator == denominator`, then the returned value
203 /// is guaranteed to be `true`. If `numerator == 0`, then the returned
204 /// value is guaranteed to be `false`.
205 ///
206 /// See also the [`Bernoulli`] distribution, which may be faster if
207 /// sampling from the same `numerator` and `denominator` repeatedly.
208 ///
209 /// # Panics
210 ///
211 /// If `denominator == 0` or `numerator > denominator`.
212 ///
213 /// # Example
214 ///
215 /// ```
216 /// use rand::Rng;
217 ///
218 /// let mut rng = rand::rng();
219 /// println!("{}", rng.random_ratio(2, 3));
220 /// ```
221 ///
222 /// [`Bernoulli`]: distr::Bernoulli
223 #[inline]
224 #[track_caller]
225 fn random_ratio(&mut self, numerator: u32, denominator: u32) -> bool {
226 match distr::Bernoulli::from_ratio(numerator, denominator) {
227 Ok(d) => self.sample(d),
228 Err(_) => panic!(
229 "p={}/{} is outside range [0.0, 1.0]",
230 numerator, denominator
231 ),
232 }
233 }
234
235 /// Sample a new value, using the given distribution.
236 ///
237 /// ### Example
238 ///
239 /// ```
240 /// use rand::Rng;
241 /// use rand::distr::Uniform;
242 ///
243 /// let mut rng = rand::rng();
244 /// let x = rng.sample(Uniform::new(10u32, 15).unwrap());
245 /// // Type annotation requires two types, the type and distribution; the
246 /// // distribution can be inferred.
247 /// let y = rng.sample::<u16, _>(Uniform::new(10, 15).unwrap());
248 /// ```
249 fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T {
250 distr.sample(self)
251 }
252
253 /// Create an iterator that generates values using the given distribution.
254 ///
255 /// Note: this method consumes its arguments. Use
256 /// `(&mut rng).sample_iter(..)` to avoid consuming the RNG.
257 ///
258 /// # Example
259 ///
260 /// ```
261 /// use rand::Rng;
262 /// use rand::distr::{Alphanumeric, Uniform, StandardUniform};
263 ///
264 /// let mut rng = rand::rng();
265 ///
266 /// // Vec of 16 x f32:
267 /// let v: Vec<f32> = (&mut rng).sample_iter(StandardUniform).take(16).collect();
268 ///
269 /// // String:
270 /// let s: String = (&mut rng).sample_iter(Alphanumeric)
271 /// .take(7)
272 /// .map(char::from)
273 /// .collect();
274 ///
275 /// // Combined values
276 /// println!("{:?}", (&mut rng).sample_iter(StandardUniform).take(5)
277 /// .collect::<Vec<(f64, bool)>>());
278 ///
279 /// // Dice-rolling:
280 /// let die_range = Uniform::new_inclusive(1, 6).unwrap();
281 /// let mut roll_die = (&mut rng).sample_iter(die_range);
282 /// while roll_die.next().unwrap() != 6 {
283 /// println!("Not a 6; rolling again!");
284 /// }
285 /// ```
286 fn sample_iter<T, D>(self, distr: D) -> distr::Iter<D, Self, T>
287 where
288 D: Distribution<T>,
289 Self: Sized,
290 {
291 distr.sample_iter(self)
292 }
293
294 /// Fill any type implementing [`Fill`] with random data
295 ///
296 /// This method is implemented for types which may be safely reinterpreted
297 /// as an (aligned) `[u8]` slice then filled with random data. It is often
298 /// faster than using [`Rng::random`] but not value-equivalent.
299 ///
300 /// The distribution is expected to be uniform with portable results, but
301 /// this cannot be guaranteed for third-party implementations.
302 ///
303 /// # Example
304 ///
305 /// ```
306 /// use rand::Rng;
307 ///
308 /// let mut arr = [0i8; 20];
309 /// rand::rng().fill(&mut arr[..]);
310 /// ```
311 ///
312 /// [`fill_bytes`]: RngCore::fill_bytes
313 #[track_caller]
314 fn fill<T: Fill + ?Sized>(&mut self, dest: &mut T) {
315 dest.fill(self)
316 }
317
318 /// Alias for [`Rng::random`].
319 #[inline]
320 #[deprecated(
321 since = "0.9.0",
322 note = "Renamed to `random` to avoid conflict with the new `gen` keyword in Rust 2024."
323 )]
324 fn r#gen<T>(&mut self) -> T
325 where
326 StandardUniform: Distribution<T>,
327 {
328 self.random()
329 }
330
331 /// Alias for [`Rng::random_range`].
332 #[inline]
333 #[deprecated(since = "0.9.0", note = "Renamed to `random_range`")]
334 fn gen_range<T, R>(&mut self, range: R) -> T
335 where
336 T: SampleUniform,
337 R: SampleRange<T>,
338 {
339 self.random_range(range)
340 }
341
342 /// Alias for [`Rng::random_bool`].
343 #[inline]
344 #[deprecated(since = "0.9.0", note = "Renamed to `random_bool`")]
345 fn gen_bool(&mut self, p: f64) -> bool {
346 self.random_bool(p)
347 }
348
349 /// Alias for [`Rng::random_ratio`].
350 #[inline]
351 #[deprecated(since = "0.9.0", note = "Renamed to `random_ratio`")]
352 fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool {
353 self.random_ratio(numerator, denominator)
354 }
355}
356
357impl<R: RngCore + ?Sized> Rng for R {}
358
359/// Types which may be filled with random data
360///
361/// This trait allows arrays to be efficiently filled with random data.
362///
363/// Implementations are expected to be portable across machines unless
364/// clearly documented otherwise (see the
365/// [Chapter on Portability](https://rust-random.github.io/book/portability.html)).
366pub trait Fill {
367 /// Fill self with random data
368 fn fill<R: Rng + ?Sized>(&mut self, rng: &mut R);
369}
370
371macro_rules! impl_fill_each {
372 () => {};
373 ($t:ty) => {
374 impl Fill for [$t] {
375 fn fill<R: Rng + ?Sized>(&mut self, rng: &mut R) {
376 for elt in self.iter_mut() {
377 *elt = rng.random();
378 }
379 }
380 }
381 };
382 ($t:ty, $($tt:ty,)*) => {
383 impl_fill_each!($t);
384 impl_fill_each!($($tt,)*);
385 };
386}
387
388impl_fill_each!(bool, char, f32, f64,);
389
390impl Fill for [u8] {
391 fn fill<R: Rng + ?Sized>(&mut self, rng: &mut R) {
392 rng.fill_bytes(self)
393 }
394}
395
396macro_rules! impl_fill {
397 () => {};
398 ($t:ty) => {
399 impl Fill for [$t] {
400 #[inline(never)] // in micro benchmarks, this improves performance
401 fn fill<R: Rng + ?Sized>(&mut self, rng: &mut R) {
402 if self.len() > 0 {
403 rng.fill_bytes(self.as_mut_bytes());
404 for x in self {
405 *x = x.to_le();
406 }
407 }
408 }
409 }
410
411 impl Fill for [Wrapping<$t>] {
412 #[inline(never)]
413 fn fill<R: Rng + ?Sized>(&mut self, rng: &mut R) {
414 if self.len() > 0 {
415 rng.fill_bytes(self.as_mut_bytes());
416 for x in self {
417 *x = Wrapping(x.0.to_le());
418 }
419 }
420 }
421 }
422 };
423 ($t:ty, $($tt:ty,)*) => {
424 impl_fill!($t);
425 // TODO: this could replace above impl once Rust #32463 is fixed
426 // impl_fill!(Wrapping<$t>);
427 impl_fill!($($tt,)*);
428 }
429}
430
431impl_fill!(u16, u32, u64, u128,);
432impl_fill!(i8, i16, i32, i64, i128,);
433
434impl<T, const N: usize> Fill for [T; N]
435where
436 [T]: Fill,
437{
438 fn fill<R: Rng + ?Sized>(&mut self, rng: &mut R) {
439 <[T] as Fill>::fill(self, rng)
440 }
441}
442
443#[cfg(test)]
444mod test {
445 use super::*;
446 use crate::rngs::mock::StepRng;
447 use crate::test::rng;
448 #[cfg(feature = "alloc")]
449 use alloc::boxed::Box;
450
451 #[test]
452 fn test_fill_bytes_default() {
453 let mut r = StepRng::new(0x11_22_33_44_55_66_77_88, 0);
454
455 // check every remainder mod 8, both in small and big vectors.
456 let lengths = [0, 1, 2, 3, 4, 5, 6, 7, 80, 81, 82, 83, 84, 85, 86, 87];
457 for &n in lengths.iter() {
458 let mut buffer = [0u8; 87];
459 let v = &mut buffer[0..n];
460 r.fill_bytes(v);
461
462 // use this to get nicer error messages.
463 for (i, &byte) in v.iter().enumerate() {
464 if byte == 0 {
465 panic!("byte {} of {} is zero", i, n)
466 }
467 }
468 }
469 }
470
471 #[test]
472 fn test_fill() {
473 let x = 9041086907909331047; // a random u64
474 let mut rng = StepRng::new(x, 0);
475
476 // Convert to byte sequence and back to u64; byte-swap twice if BE.
477 let mut array = [0u64; 2];
478 rng.fill(&mut array[..]);
479 assert_eq!(array, [x, x]);
480 assert_eq!(rng.next_u64(), x);
481
482 // Convert to bytes then u32 in LE order
483 let mut array = [0u32; 2];
484 rng.fill(&mut array[..]);
485 assert_eq!(array, [x as u32, (x >> 32) as u32]);
486 assert_eq!(rng.next_u32(), x as u32);
487
488 // Check equivalence using wrapped arrays
489 let mut warray = [Wrapping(0u32); 2];
490 rng.fill(&mut warray[..]);
491 assert_eq!(array[0], warray[0].0);
492 assert_eq!(array[1], warray[1].0);
493
494 // Check equivalence for generated floats
495 let mut array = [0f32; 2];
496 rng.fill(&mut array);
497 let arr2: [f32; 2] = rng.random();
498 assert_eq!(array, arr2);
499 }
500
501 #[test]
502 fn test_fill_empty() {
503 let mut array = [0u32; 0];
504 let mut rng = StepRng::new(0, 1);
505 rng.fill(&mut array);
506 rng.fill(&mut array[..]);
507 }
508
509 #[test]
510 fn test_random_range_int() {
511 let mut r = rng(101);
512 for _ in 0..1000 {
513 let a = r.random_range(-4711..17);
514 assert!((-4711..17).contains(&a));
515 let a: i8 = r.random_range(-3..42);
516 assert!((-3..42).contains(&a));
517 let a: u16 = r.random_range(10..99);
518 assert!((10..99).contains(&a));
519 let a: i32 = r.random_range(-100..2000);
520 assert!((-100..2000).contains(&a));
521 let a: u32 = r.random_range(12..=24);
522 assert!((12..=24).contains(&a));
523
524 assert_eq!(r.random_range(..1u32), 0u32);
525 assert_eq!(r.random_range(-12i64..-11), -12i64);
526 assert_eq!(r.random_range(3_000_000..3_000_001), 3_000_000);
527 }
528 }
529
530 #[test]
531 fn test_random_range_float() {
532 let mut r = rng(101);
533 for _ in 0..1000 {
534 let a = r.random_range(-4.5..1.7);
535 assert!((-4.5..1.7).contains(&a));
536 let a = r.random_range(-1.1..=-0.3);
537 assert!((-1.1..=-0.3).contains(&a));
538
539 assert_eq!(r.random_range(0.0f32..=0.0), 0.);
540 assert_eq!(r.random_range(-11.0..=-11.0), -11.);
541 assert_eq!(r.random_range(3_000_000.0..=3_000_000.0), 3_000_000.);
542 }
543 }
544
545 #[test]
546 #[should_panic]
547 #[allow(clippy::reversed_empty_ranges)]
548 fn test_random_range_panic_int() {
549 let mut r = rng(102);
550 r.random_range(5..-2);
551 }
552
553 #[test]
554 #[should_panic]
555 #[allow(clippy::reversed_empty_ranges)]
556 fn test_random_range_panic_usize() {
557 let mut r = rng(103);
558 r.random_range(5..2);
559 }
560
561 #[test]
562 #[allow(clippy::bool_assert_comparison)]
563 fn test_random_bool() {
564 let mut r = rng(105);
565 for _ in 0..5 {
566 assert_eq!(r.random_bool(0.0), false);
567 assert_eq!(r.random_bool(1.0), true);
568 }
569 }
570
571 #[test]
572 fn test_rng_mut_ref() {
573 fn use_rng(mut r: impl Rng) {
574 let _ = r.next_u32();
575 }
576
577 let mut rng = rng(109);
578 use_rng(&mut rng);
579 }
580
581 #[test]
582 fn test_rng_trait_object() {
583 use crate::distr::{Distribution, StandardUniform};
584 let mut rng = rng(109);
585 let mut r = &mut rng as &mut dyn RngCore;
586 r.next_u32();
587 r.random::<i32>();
588 assert_eq!(r.random_range(0..1), 0);
589 let _c: u8 = StandardUniform.sample(&mut r);
590 }
591
592 #[test]
593 #[cfg(feature = "alloc")]
594 fn test_rng_boxed_trait() {
595 use crate::distr::{Distribution, StandardUniform};
596 let rng = rng(110);
597 let mut r = Box::new(rng) as Box<dyn RngCore>;
598 r.next_u32();
599 r.random::<i32>();
600 assert_eq!(r.random_range(0..1), 0);
601 let _c: u8 = StandardUniform.sample(&mut r);
602 }
603
604 #[test]
605 #[cfg_attr(miri, ignore)] // Miri is too slow
606 fn test_gen_ratio_average() {
607 const NUM: u32 = 3;
608 const DENOM: u32 = 10;
609 const N: u32 = 100_000;
610
611 let mut sum: u32 = 0;
612 let mut rng = rng(111);
613 for _ in 0..N {
614 if rng.random_ratio(NUM, DENOM) {
615 sum += 1;
616 }
617 }
618 // Have Binomial(N, NUM/DENOM) distribution
619 let expected = (NUM * N) / DENOM; // exact integer
620 assert!(((sum - expected) as i32).abs() < 500);
621 }
622}
623