1// Copyright 2018-2020 Developers of the Rand project.
2// Copyright 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//! A distribution uniformly sampling numbers within a given range.
11//!
12//! [`Uniform`] is the standard distribution to sample uniformly from a range;
13//! e.g. `Uniform::new_inclusive(1, 6).unwrap()` can sample integers from 1 to 6, like a
14//! standard die. [`Rng::random_range`] is implemented over [`Uniform`].
15//!
16//! # Example usage
17//!
18//! ```
19//! use rand::Rng;
20//! use rand::distr::Uniform;
21//!
22//! let mut rng = rand::rng();
23//! let side = Uniform::new(-10.0, 10.0).unwrap();
24//!
25//! // sample between 1 and 10 points
26//! for _ in 0..rng.random_range(1..=10) {
27//! // sample a point from the square with sides -10 - 10 in two dimensions
28//! let (x, y) = (rng.sample(side), rng.sample(side));
29//! println!("Point: {}, {}", x, y);
30//! }
31//! ```
32//!
33//! # Extending `Uniform` to support a custom type
34//!
35//! To extend [`Uniform`] to support your own types, write a back-end which
36//! implements the [`UniformSampler`] trait, then implement the [`SampleUniform`]
37//! helper trait to "register" your back-end. See the `MyF32` example below.
38//!
39//! At a minimum, the back-end needs to store any parameters needed for sampling
40//! (e.g. the target range) and implement `new`, `new_inclusive` and `sample`.
41//! Those methods should include an assertion to check the range is valid (i.e.
42//! `low < high`). The example below merely wraps another back-end.
43//!
44//! The `new`, `new_inclusive`, `sample_single` and `sample_single_inclusive`
45//! functions use arguments of
46//! type `SampleBorrow<X>` to support passing in values by reference or
47//! by value. In the implementation of these functions, you can choose to
48//! simply use the reference returned by [`SampleBorrow::borrow`], or you can choose
49//! to copy or clone the value, whatever is appropriate for your type.
50//!
51//! ```
52//! use rand::prelude::*;
53//! use rand::distr::uniform::{Uniform, SampleUniform,
54//! UniformSampler, UniformFloat, SampleBorrow, Error};
55//!
56//! struct MyF32(f32);
57//!
58//! #[derive(Clone, Copy, Debug)]
59//! struct UniformMyF32(UniformFloat<f32>);
60//!
61//! impl UniformSampler for UniformMyF32 {
62//! type X = MyF32;
63//!
64//! fn new<B1, B2>(low: B1, high: B2) -> Result<Self, Error>
65//! where B1: SampleBorrow<Self::X> + Sized,
66//! B2: SampleBorrow<Self::X> + Sized
67//! {
68//! UniformFloat::<f32>::new(low.borrow().0, high.borrow().0).map(UniformMyF32)
69//! }
70//! fn new_inclusive<B1, B2>(low: B1, high: B2) -> Result<Self, Error>
71//! where B1: SampleBorrow<Self::X> + Sized,
72//! B2: SampleBorrow<Self::X> + Sized
73//! {
74//! UniformFloat::<f32>::new_inclusive(low.borrow().0, high.borrow().0).map(UniformMyF32)
75//! }
76//! fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
77//! MyF32(self.0.sample(rng))
78//! }
79//! }
80//!
81//! impl SampleUniform for MyF32 {
82//! type Sampler = UniformMyF32;
83//! }
84//!
85//! let (low, high) = (MyF32(17.0f32), MyF32(22.0f32));
86//! let uniform = Uniform::new(low, high).unwrap();
87//! let x = uniform.sample(&mut rand::rng());
88//! ```
89//!
90//! [`SampleUniform`]: crate::distr::uniform::SampleUniform
91//! [`UniformSampler`]: crate::distr::uniform::UniformSampler
92//! [`UniformInt`]: crate::distr::uniform::UniformInt
93//! [`UniformFloat`]: crate::distr::uniform::UniformFloat
94//! [`UniformDuration`]: crate::distr::uniform::UniformDuration
95//! [`SampleBorrow::borrow`]: crate::distr::uniform::SampleBorrow::borrow
96
97#[path = "uniform_float.rs"]
98mod float;
99#[doc(inline)]
100pub use float::UniformFloat;
101
102#[path = "uniform_int.rs"]
103mod int;
104#[doc(inline)]
105pub use int::{UniformInt, UniformUsize};
106
107#[path = "uniform_other.rs"]
108mod other;
109#[doc(inline)]
110pub use other::{UniformChar, UniformDuration};
111
112use core::fmt;
113use core::ops::{Range, RangeInclusive, RangeTo, RangeToInclusive};
114
115use crate::distr::Distribution;
116use crate::{Rng, RngCore};
117
118/// Error type returned from [`Uniform::new`] and `new_inclusive`.
119#[derive(Clone, Copy, Debug, PartialEq, Eq)]
120pub enum Error {
121 /// `low > high`, or equal in case of exclusive range.
122 EmptyRange,
123 /// Input or range `high - low` is non-finite. Not relevant to integer types.
124 NonFinite,
125}
126
127impl fmt::Display for Error {
128 fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
129 f.write_str(data:match self {
130 Error::EmptyRange => "low > high (or equal if exclusive) in uniform distribution",
131 Error::NonFinite => "Non-finite range in uniform distribution",
132 })
133 }
134}
135
136#[cfg(feature = "std")]
137impl std::error::Error for Error {}
138
139#[cfg(feature = "serde")]
140use serde::{Deserialize, Serialize};
141
142/// Sample values uniformly between two bounds.
143///
144/// # Construction
145///
146/// [`Uniform::new`] and [`Uniform::new_inclusive`] construct a uniform
147/// distribution sampling from the given `low` and `high` limits. `Uniform` may
148/// also be constructed via [`TryFrom`] as in `Uniform::try_from(1..=6).unwrap()`.
149///
150/// Constructors may do extra work up front to allow faster sampling of multiple
151/// values. Where only a single sample is required it is suggested to use
152/// [`Rng::random_range`] or one of the `sample_single` methods instead.
153///
154/// When sampling from a constant range, many calculations can happen at
155/// compile-time and all methods should be fast; for floating-point ranges and
156/// the full range of integer types, this should have comparable performance to
157/// the [`StandardUniform`](super::StandardUniform) distribution.
158///
159/// # Provided implementations
160///
161/// - `char` ([`UniformChar`]): samples a range over the implementation for `u32`
162/// - `f32`, `f64` ([`UniformFloat`]): samples approximately uniformly within a
163/// range; bias may be present in the least-significant bit of the significand
164/// and the limits of the input range may be sampled even when an open
165/// (exclusive) range is used
166/// - Integer types ([`UniformInt`]) may show a small bias relative to the
167/// expected uniform distribution of output. In the worst case, bias affects
168/// 1 in `2^n` samples where n is 56 (`i8` and `u8`), 48 (`i16` and `u16`), 96
169/// (`i32` and `u32`), 64 (`i64` and `u64`), 128 (`i128` and `u128`).
170/// The `unbiased` feature flag fixes this bias.
171/// - `usize` ([`UniformUsize`]) is handled specially, using the `u32`
172/// implementation where possible to enable portable results across 32-bit and
173/// 64-bit CPU architectures.
174/// - `Duration` ([`UniformDuration`]): samples a range over the implementation
175/// for `u32` or `u64`
176/// - SIMD types (requires [`simd_support`] feature) like x86's [`__m128i`]
177/// and `std::simd`'s [`u32x4`], [`f32x4`] and [`mask32x4`] types are
178/// effectively arrays of integer or floating-point types. Each lane is
179/// sampled independently from its own range, potentially with more efficient
180/// random-bit-usage than would be achieved with sequential sampling.
181///
182/// # Example
183///
184/// ```
185/// use rand::distr::{Distribution, Uniform};
186///
187/// let between = Uniform::try_from(10..10000).unwrap();
188/// let mut rng = rand::rng();
189/// let mut sum = 0;
190/// for _ in 0..1000 {
191/// sum += between.sample(&mut rng);
192/// }
193/// println!("{}", sum);
194/// ```
195///
196/// For a single sample, [`Rng::random_range`] may be preferred:
197///
198/// ```
199/// use rand::Rng;
200///
201/// let mut rng = rand::rng();
202/// println!("{}", rng.random_range(0..10));
203/// ```
204///
205/// [`new`]: Uniform::new
206/// [`new_inclusive`]: Uniform::new_inclusive
207/// [`Rng::random_range`]: Rng::random_range
208/// [`__m128i`]: https://doc.rust-lang.org/core/arch/x86/struct.__m128i.html
209/// [`u32x4`]: std::simd::u32x4
210/// [`f32x4`]: std::simd::f32x4
211/// [`mask32x4`]: std::simd::mask32x4
212/// [`simd_support`]: https://github.com/rust-random/rand#crate-features
213#[derive(Clone, Copy, Debug, PartialEq, Eq)]
214#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
215#[cfg_attr(feature = "serde", serde(bound(serialize = "X::Sampler: Serialize")))]
216#[cfg_attr(
217 feature = "serde",
218 serde(bound(deserialize = "X::Sampler: Deserialize<'de>"))
219)]
220pub struct Uniform<X: SampleUniform>(X::Sampler);
221
222impl<X: SampleUniform> Uniform<X> {
223 /// Create a new `Uniform` instance, which samples uniformly from the half
224 /// open range `[low, high)` (excluding `high`).
225 ///
226 /// For discrete types (e.g. integers), samples will always be strictly less
227 /// than `high`. For (approximations of) continuous types (e.g. `f32`, `f64`),
228 /// samples may equal `high` due to loss of precision but may not be
229 /// greater than `high`.
230 ///
231 /// Fails if `low >= high`, or if `low`, `high` or the range `high - low` is
232 /// non-finite. In release mode, only the range is checked.
233 pub fn new<B1, B2>(low: B1, high: B2) -> Result<Uniform<X>, Error>
234 where
235 B1: SampleBorrow<X> + Sized,
236 B2: SampleBorrow<X> + Sized,
237 {
238 X::Sampler::new(low, high).map(Uniform)
239 }
240
241 /// Create a new `Uniform` instance, which samples uniformly from the closed
242 /// range `[low, high]` (inclusive).
243 ///
244 /// Fails if `low > high`, or if `low`, `high` or the range `high - low` is
245 /// non-finite. In release mode, only the range is checked.
246 pub fn new_inclusive<B1, B2>(low: B1, high: B2) -> Result<Uniform<X>, Error>
247 where
248 B1: SampleBorrow<X> + Sized,
249 B2: SampleBorrow<X> + Sized,
250 {
251 X::Sampler::new_inclusive(low, high).map(Uniform)
252 }
253}
254
255impl<X: SampleUniform> Distribution<X> for Uniform<X> {
256 fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> X {
257 self.0.sample(rng)
258 }
259}
260
261/// Helper trait for creating objects using the correct implementation of
262/// [`UniformSampler`] for the sampling type.
263///
264/// See the [module documentation] on how to implement [`Uniform`] range
265/// sampling for a custom type.
266///
267/// [module documentation]: crate::distr::uniform
268pub trait SampleUniform: Sized {
269 /// The `UniformSampler` implementation supporting type `X`.
270 type Sampler: UniformSampler<X = Self>;
271}
272
273/// Helper trait handling actual uniform sampling.
274///
275/// See the [module documentation] on how to implement [`Uniform`] range
276/// sampling for a custom type.
277///
278/// Implementation of [`sample_single`] is optional, and is only useful when
279/// the implementation can be faster than `Self::new(low, high).sample(rng)`.
280///
281/// [module documentation]: crate::distr::uniform
282/// [`sample_single`]: UniformSampler::sample_single
283pub trait UniformSampler: Sized {
284 /// The type sampled by this implementation.
285 type X;
286
287 /// Construct self, with inclusive lower bound and exclusive upper bound `[low, high)`.
288 ///
289 /// For discrete types (e.g. integers), samples will always be strictly less
290 /// than `high`. For (approximations of) continuous types (e.g. `f32`, `f64`),
291 /// samples may equal `high` due to loss of precision but may not be
292 /// greater than `high`.
293 ///
294 /// Usually users should not call this directly but prefer to use
295 /// [`Uniform::new`].
296 fn new<B1, B2>(low: B1, high: B2) -> Result<Self, Error>
297 where
298 B1: SampleBorrow<Self::X> + Sized,
299 B2: SampleBorrow<Self::X> + Sized;
300
301 /// Construct self, with inclusive bounds `[low, high]`.
302 ///
303 /// Usually users should not call this directly but prefer to use
304 /// [`Uniform::new_inclusive`].
305 fn new_inclusive<B1, B2>(low: B1, high: B2) -> Result<Self, Error>
306 where
307 B1: SampleBorrow<Self::X> + Sized,
308 B2: SampleBorrow<Self::X> + Sized;
309
310 /// Sample a value.
311 fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X;
312
313 /// Sample a single value uniformly from a range with inclusive lower bound
314 /// and exclusive upper bound `[low, high)`.
315 ///
316 /// For discrete types (e.g. integers), samples will always be strictly less
317 /// than `high`. For (approximations of) continuous types (e.g. `f32`, `f64`),
318 /// samples may equal `high` due to loss of precision but may not be
319 /// greater than `high`.
320 ///
321 /// By default this is implemented using
322 /// `UniformSampler::new(low, high).sample(rng)`. However, for some types
323 /// more optimal implementations for single usage may be provided via this
324 /// method (which is the case for integers and floats).
325 /// Results may not be identical.
326 ///
327 /// Note that to use this method in a generic context, the type needs to be
328 /// retrieved via `SampleUniform::Sampler` as follows:
329 /// ```
330 /// use rand::distr::uniform::{SampleUniform, UniformSampler};
331 /// # #[allow(unused)]
332 /// fn sample_from_range<T: SampleUniform>(lb: T, ub: T) -> T {
333 /// let mut rng = rand::rng();
334 /// <T as SampleUniform>::Sampler::sample_single(lb, ub, &mut rng).unwrap()
335 /// }
336 /// ```
337 fn sample_single<R: Rng + ?Sized, B1, B2>(
338 low: B1,
339 high: B2,
340 rng: &mut R,
341 ) -> Result<Self::X, Error>
342 where
343 B1: SampleBorrow<Self::X> + Sized,
344 B2: SampleBorrow<Self::X> + Sized,
345 {
346 let uniform: Self = UniformSampler::new(low, high)?;
347 Ok(uniform.sample(rng))
348 }
349
350 /// Sample a single value uniformly from a range with inclusive lower bound
351 /// and inclusive upper bound `[low, high]`.
352 ///
353 /// By default this is implemented using
354 /// `UniformSampler::new_inclusive(low, high).sample(rng)`. However, for
355 /// some types more optimal implementations for single usage may be provided
356 /// via this method.
357 /// Results may not be identical.
358 fn sample_single_inclusive<R: Rng + ?Sized, B1, B2>(
359 low: B1,
360 high: B2,
361 rng: &mut R,
362 ) -> Result<Self::X, Error>
363 where
364 B1: SampleBorrow<Self::X> + Sized,
365 B2: SampleBorrow<Self::X> + Sized,
366 {
367 let uniform: Self = UniformSampler::new_inclusive(low, high)?;
368 Ok(uniform.sample(rng))
369 }
370}
371
372impl<X: SampleUniform> TryFrom<Range<X>> for Uniform<X> {
373 type Error = Error;
374
375 fn try_from(r: Range<X>) -> Result<Uniform<X>, Error> {
376 Uniform::new(low:r.start, high:r.end)
377 }
378}
379
380impl<X: SampleUniform> TryFrom<RangeInclusive<X>> for Uniform<X> {
381 type Error = Error;
382
383 fn try_from(r: ::core::ops::RangeInclusive<X>) -> Result<Uniform<X>, Error> {
384 Uniform::new_inclusive(low:r.start(), high:r.end())
385 }
386}
387
388/// Helper trait similar to [`Borrow`] but implemented
389/// only for [`SampleUniform`] and references to [`SampleUniform`]
390/// in order to resolve ambiguity issues.
391///
392/// [`Borrow`]: std::borrow::Borrow
393pub trait SampleBorrow<Borrowed> {
394 /// Immutably borrows from an owned value. See [`Borrow::borrow`]
395 ///
396 /// [`Borrow::borrow`]: std::borrow::Borrow::borrow
397 fn borrow(&self) -> &Borrowed;
398}
399impl<Borrowed> SampleBorrow<Borrowed> for Borrowed
400where
401 Borrowed: SampleUniform,
402{
403 #[inline(always)]
404 fn borrow(&self) -> &Borrowed {
405 self
406 }
407}
408impl<Borrowed> SampleBorrow<Borrowed> for &Borrowed
409where
410 Borrowed: SampleUniform,
411{
412 #[inline(always)]
413 fn borrow(&self) -> &Borrowed {
414 self
415 }
416}
417
418/// Range that supports generating a single sample efficiently.
419///
420/// Any type implementing this trait can be used to specify the sampled range
421/// for `Rng::random_range`.
422pub trait SampleRange<T> {
423 /// Generate a sample from the given range.
424 fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> Result<T, Error>;
425
426 /// Check whether the range is empty.
427 fn is_empty(&self) -> bool;
428}
429
430impl<T: SampleUniform + PartialOrd> SampleRange<T> for Range<T> {
431 #[inline]
432 fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> Result<T, Error> {
433 T::Sampler::sample_single(self.start, self.end, rng)
434 }
435
436 #[inline]
437 fn is_empty(&self) -> bool {
438 !(self.start < self.end)
439 }
440}
441
442impl<T: SampleUniform + PartialOrd> SampleRange<T> for RangeInclusive<T> {
443 #[inline]
444 fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> Result<T, Error> {
445 T::Sampler::sample_single_inclusive(self.start(), self.end(), rng)
446 }
447
448 #[inline]
449 fn is_empty(&self) -> bool {
450 !(self.start() <= self.end())
451 }
452}
453
454macro_rules! impl_sample_range_u {
455 ($t:ty) => {
456 impl SampleRange<$t> for RangeTo<$t> {
457 #[inline]
458 fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> Result<$t, Error> {
459 <$t as SampleUniform>::Sampler::sample_single(0, self.end, rng)
460 }
461
462 #[inline]
463 fn is_empty(&self) -> bool {
464 0 == self.end
465 }
466 }
467
468 impl SampleRange<$t> for RangeToInclusive<$t> {
469 #[inline]
470 fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> Result<$t, Error> {
471 <$t as SampleUniform>::Sampler::sample_single_inclusive(0, self.end, rng)
472 }
473
474 #[inline]
475 fn is_empty(&self) -> bool {
476 false
477 }
478 }
479 };
480}
481
482impl_sample_range_u!(u8);
483impl_sample_range_u!(u16);
484impl_sample_range_u!(u32);
485impl_sample_range_u!(u64);
486impl_sample_range_u!(u128);
487impl_sample_range_u!(usize);
488
489#[cfg(test)]
490mod tests {
491 use super::*;
492 use core::time::Duration;
493
494 #[test]
495 #[cfg(feature = "serde")]
496 fn test_uniform_serialization() {
497 let unit_box: Uniform<i32> = Uniform::new(-1, 1).unwrap();
498 let de_unit_box: Uniform<i32> =
499 bincode::deserialize(&bincode::serialize(&unit_box).unwrap()).unwrap();
500 assert_eq!(unit_box.0, de_unit_box.0);
501
502 let unit_box: Uniform<f32> = Uniform::new(-1., 1.).unwrap();
503 let de_unit_box: Uniform<f32> =
504 bincode::deserialize(&bincode::serialize(&unit_box).unwrap()).unwrap();
505 assert_eq!(unit_box.0, de_unit_box.0);
506 }
507
508 #[test]
509 fn test_custom_uniform() {
510 use crate::distr::uniform::{SampleBorrow, SampleUniform, UniformFloat, UniformSampler};
511 #[derive(Clone, Copy, PartialEq, PartialOrd)]
512 struct MyF32 {
513 x: f32,
514 }
515 #[derive(Clone, Copy, Debug)]
516 struct UniformMyF32(UniformFloat<f32>);
517 impl UniformSampler for UniformMyF32 {
518 type X = MyF32;
519
520 fn new<B1, B2>(low: B1, high: B2) -> Result<Self, Error>
521 where
522 B1: SampleBorrow<Self::X> + Sized,
523 B2: SampleBorrow<Self::X> + Sized,
524 {
525 UniformFloat::<f32>::new(low.borrow().x, high.borrow().x).map(UniformMyF32)
526 }
527
528 fn new_inclusive<B1, B2>(low: B1, high: B2) -> Result<Self, Error>
529 where
530 B1: SampleBorrow<Self::X> + Sized,
531 B2: SampleBorrow<Self::X> + Sized,
532 {
533 UniformSampler::new(low, high)
534 }
535
536 fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
537 MyF32 {
538 x: self.0.sample(rng),
539 }
540 }
541 }
542 impl SampleUniform for MyF32 {
543 type Sampler = UniformMyF32;
544 }
545
546 let (low, high) = (MyF32 { x: 17.0f32 }, MyF32 { x: 22.0f32 });
547 let uniform = Uniform::new(low, high).unwrap();
548 let mut rng = crate::test::rng(804);
549 for _ in 0..100 {
550 let x: MyF32 = rng.sample(uniform);
551 assert!(low <= x && x < high);
552 }
553 }
554
555 #[test]
556 fn value_stability() {
557 fn test_samples<T: SampleUniform + Copy + fmt::Debug + PartialEq>(
558 lb: T,
559 ub: T,
560 expected_single: &[T],
561 expected_multiple: &[T],
562 ) where
563 Uniform<T>: Distribution<T>,
564 {
565 let mut rng = crate::test::rng(897);
566 let mut buf = [lb; 3];
567
568 for x in &mut buf {
569 *x = T::Sampler::sample_single(lb, ub, &mut rng).unwrap();
570 }
571 assert_eq!(&buf, expected_single);
572
573 let distr = Uniform::new(lb, ub).unwrap();
574 for x in &mut buf {
575 *x = rng.sample(&distr);
576 }
577 assert_eq!(&buf, expected_multiple);
578 }
579
580 test_samples(
581 0f32,
582 1e-2f32,
583 &[0.0003070104, 0.0026630748, 0.00979833],
584 &[0.008194133, 0.00398172, 0.007428536],
585 );
586 test_samples(
587 -1e10f64,
588 1e10f64,
589 &[-4673848682.871551, 6388267422.932352, 4857075081.198343],
590 &[1173375212.1808167, 1917642852.109581, 2365076174.3153973],
591 );
592
593 test_samples(
594 Duration::new(2, 0),
595 Duration::new(4, 0),
596 &[
597 Duration::new(2, 532615131),
598 Duration::new(3, 638826742),
599 Duration::new(3, 485707508),
600 ],
601 &[
602 Duration::new(3, 117337521),
603 Duration::new(3, 191764285),
604 Duration::new(3, 236507617),
605 ],
606 );
607 }
608
609 #[test]
610 fn uniform_distributions_can_be_compared() {
611 assert_eq!(
612 Uniform::new(1.0, 2.0).unwrap(),
613 Uniform::new(1.0, 2.0).unwrap()
614 );
615
616 // To cover UniformInt
617 assert_eq!(
618 Uniform::new(1_u32, 2_u32).unwrap(),
619 Uniform::new(1_u32, 2_u32).unwrap()
620 );
621 }
622}
623