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//! `UniformInt` implementation
11
12use super::{Error, SampleBorrow, SampleUniform, UniformSampler};
13use crate::distr::utils::WideningMultiply;
14#[cfg(feature = "simd_support")]
15use crate::distr::{Distribution, StandardUniform};
16use crate::Rng;
17
18#[cfg(feature = "simd_support")]
19use core::simd::prelude::*;
20#[cfg(feature = "simd_support")]
21use core::simd::{LaneCount, SupportedLaneCount};
22
23#[cfg(feature = "serde")]
24use serde::{Deserialize, Serialize};
25
26/// The back-end implementing [`UniformSampler`] for integer types.
27///
28/// Unless you are implementing [`UniformSampler`] for your own type, this type
29/// should not be used directly, use [`Uniform`] instead.
30///
31/// # Implementation notes
32///
33/// For simplicity, we use the same generic struct `UniformInt<X>` for all
34/// integer types `X`. This gives us only one field type, `X`; to store unsigned
35/// values of this size, we take use the fact that these conversions are no-ops.
36///
37/// For a closed range, the number of possible numbers we should generate is
38/// `range = (high - low + 1)`. To avoid bias, we must ensure that the size of
39/// our sample space, `zone`, is a multiple of `range`; other values must be
40/// rejected (by replacing with a new random sample).
41///
42/// As a special case, we use `range = 0` to represent the full range of the
43/// result type (i.e. for `new_inclusive($ty::MIN, $ty::MAX)`).
44///
45/// The optimum `zone` is the largest product of `range` which fits in our
46/// (unsigned) target type. We calculate this by calculating how many numbers we
47/// must reject: `reject = (MAX + 1) % range = (MAX - range + 1) % range`. Any (large)
48/// product of `range` will suffice, thus in `sample_single` we multiply by a
49/// power of 2 via bit-shifting (faster but may cause more rejections).
50///
51/// The smallest integer PRNGs generate is `u32`. For 8- and 16-bit outputs we
52/// use `u32` for our `zone` and samples (because it's not slower and because
53/// it reduces the chance of having to reject a sample). In this case we cannot
54/// store `zone` in the target type since it is too large, however we know
55/// `ints_to_reject < range <= $uty::MAX`.
56///
57/// An alternative to using a modulus is widening multiply: After a widening
58/// multiply by `range`, the result is in the high word. Then comparing the low
59/// word against `zone` makes sure our distribution is uniform.
60///
61/// # Bias
62///
63/// Unless the `unbiased` feature flag is used, outputs may have a small bias.
64/// In the worst case, bias affects 1 in `2^n` samples where n is
65/// 56 (`i8` and `u8`), 48 (`i16` and `u16`), 96 (`i32` and `u32`), 64 (`i64`
66/// and `u64`), 128 (`i128` and `u128`).
67///
68/// [`Uniform`]: super::Uniform
69#[derive(Clone, Copy, Debug, PartialEq, Eq)]
70#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
71pub struct UniformInt<X> {
72 pub(super) low: X,
73 pub(super) range: X,
74 thresh: X, // effectively 2.pow(max(64, uty_bits)) % range
75}
76
77macro_rules! uniform_int_impl {
78 ($ty:ty, $uty:ty, $sample_ty:ident) => {
79 impl SampleUniform for $ty {
80 type Sampler = UniformInt<$ty>;
81 }
82
83 impl UniformSampler for UniformInt<$ty> {
84 // We play free and fast with unsigned vs signed here
85 // (when $ty is signed), but that's fine, since the
86 // contract of this macro is for $ty and $uty to be
87 // "bit-equal", so casting between them is a no-op.
88
89 type X = $ty;
90
91 #[inline] // if the range is constant, this helps LLVM to do the
92 // calculations at compile-time.
93 fn new<B1, B2>(low_b: B1, high_b: B2) -> Result<Self, Error>
94 where
95 B1: SampleBorrow<Self::X> + Sized,
96 B2: SampleBorrow<Self::X> + Sized,
97 {
98 let low = *low_b.borrow();
99 let high = *high_b.borrow();
100 if !(low < high) {
101 return Err(Error::EmptyRange);
102 }
103 UniformSampler::new_inclusive(low, high - 1)
104 }
105
106 #[inline] // if the range is constant, this helps LLVM to do the
107 // calculations at compile-time.
108 fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Result<Self, Error>
109 where
110 B1: SampleBorrow<Self::X> + Sized,
111 B2: SampleBorrow<Self::X> + Sized,
112 {
113 let low = *low_b.borrow();
114 let high = *high_b.borrow();
115 if !(low <= high) {
116 return Err(Error::EmptyRange);
117 }
118
119 let range = high.wrapping_sub(low).wrapping_add(1) as $uty;
120 let thresh = if range > 0 {
121 let range = $sample_ty::from(range);
122 (range.wrapping_neg() % range)
123 } else {
124 0
125 };
126
127 Ok(UniformInt {
128 low,
129 range: range as $ty, // type: $uty
130 thresh: thresh as $uty as $ty, // type: $sample_ty
131 })
132 }
133
134 /// Sample from distribution, Lemire's method, unbiased
135 #[inline]
136 fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
137 let range = self.range as $uty as $sample_ty;
138 if range == 0 {
139 return rng.random();
140 }
141
142 let thresh = self.thresh as $uty as $sample_ty;
143 let hi = loop {
144 let (hi, lo) = rng.random::<$sample_ty>().wmul(range);
145 if lo >= thresh {
146 break hi;
147 }
148 };
149 self.low.wrapping_add(hi as $ty)
150 }
151
152 #[inline]
153 fn sample_single<R: Rng + ?Sized, B1, B2>(
154 low_b: B1,
155 high_b: B2,
156 rng: &mut R,
157 ) -> Result<Self::X, Error>
158 where
159 B1: SampleBorrow<Self::X> + Sized,
160 B2: SampleBorrow<Self::X> + Sized,
161 {
162 let low = *low_b.borrow();
163 let high = *high_b.borrow();
164 if !(low < high) {
165 return Err(Error::EmptyRange);
166 }
167 Self::sample_single_inclusive(low, high - 1, rng)
168 }
169
170 /// Sample single value, Canon's method, biased
171 ///
172 /// In the worst case, bias affects 1 in `2^n` samples where n is
173 /// 56 (`i8`), 48 (`i16`), 96 (`i32`), 64 (`i64`), 128 (`i128`).
174 #[cfg(not(feature = "unbiased"))]
175 #[inline]
176 fn sample_single_inclusive<R: Rng + ?Sized, B1, B2>(
177 low_b: B1,
178 high_b: B2,
179 rng: &mut R,
180 ) -> Result<Self::X, Error>
181 where
182 B1: SampleBorrow<Self::X> + Sized,
183 B2: SampleBorrow<Self::X> + Sized,
184 {
185 let low = *low_b.borrow();
186 let high = *high_b.borrow();
187 if !(low <= high) {
188 return Err(Error::EmptyRange);
189 }
190 let range = high.wrapping_sub(low).wrapping_add(1) as $uty as $sample_ty;
191 if range == 0 {
192 // Range is MAX+1 (unrepresentable), so we need a special case
193 return Ok(rng.random());
194 }
195
196 // generate a sample using a sensible integer type
197 let (mut result, lo_order) = rng.random::<$sample_ty>().wmul(range);
198
199 // if the sample is biased...
200 if lo_order > range.wrapping_neg() {
201 // ...generate a new sample to reduce bias...
202 let (new_hi_order, _) = (rng.random::<$sample_ty>()).wmul(range as $sample_ty);
203 // ... incrementing result on overflow
204 let is_overflow = lo_order.checked_add(new_hi_order as $sample_ty).is_none();
205 result += is_overflow as $sample_ty;
206 }
207
208 Ok(low.wrapping_add(result as $ty))
209 }
210
211 /// Sample single value, Canon's method, unbiased
212 #[cfg(feature = "unbiased")]
213 #[inline]
214 fn sample_single_inclusive<R: Rng + ?Sized, B1, B2>(
215 low_b: B1,
216 high_b: B2,
217 rng: &mut R,
218 ) -> Result<Self::X, Error>
219 where
220 B1: SampleBorrow<$ty> + Sized,
221 B2: SampleBorrow<$ty> + Sized,
222 {
223 let low = *low_b.borrow();
224 let high = *high_b.borrow();
225 if !(low <= high) {
226 return Err(Error::EmptyRange);
227 }
228 let range = high.wrapping_sub(low).wrapping_add(1) as $uty as $sample_ty;
229 if range == 0 {
230 // Range is MAX+1 (unrepresentable), so we need a special case
231 return Ok(rng.random());
232 }
233
234 let (mut result, mut lo) = rng.random::<$sample_ty>().wmul(range);
235
236 // In contrast to the biased sampler, we use a loop:
237 while lo > range.wrapping_neg() {
238 let (new_hi, new_lo) = (rng.random::<$sample_ty>()).wmul(range);
239 match lo.checked_add(new_hi) {
240 Some(x) if x < $sample_ty::MAX => {
241 // Anything less than MAX: last term is 0
242 break;
243 }
244 None => {
245 // Overflow: last term is 1
246 result += 1;
247 break;
248 }
249 _ => {
250 // Unlikely case: must check next sample
251 lo = new_lo;
252 continue;
253 }
254 }
255 }
256
257 Ok(low.wrapping_add(result as $ty))
258 }
259 }
260 };
261}
262
263uniform_int_impl! { i8, u8, u32 }
264uniform_int_impl! { i16, u16, u32 }
265uniform_int_impl! { i32, u32, u32 }
266uniform_int_impl! { i64, u64, u64 }
267uniform_int_impl! { i128, u128, u128 }
268uniform_int_impl! { u8, u8, u32 }
269uniform_int_impl! { u16, u16, u32 }
270uniform_int_impl! { u32, u32, u32 }
271uniform_int_impl! { u64, u64, u64 }
272uniform_int_impl! { u128, u128, u128 }
273
274#[cfg(feature = "simd_support")]
275macro_rules! uniform_simd_int_impl {
276 ($ty:ident, $unsigned:ident) => {
277 // The "pick the largest zone that can fit in an `u32`" optimization
278 // is less useful here. Multiple lanes complicate things, we don't
279 // know the PRNG's minimal output size, and casting to a larger vector
280 // is generally a bad idea for SIMD performance. The user can still
281 // implement it manually.
282
283 #[cfg(feature = "simd_support")]
284 impl<const LANES: usize> SampleUniform for Simd<$ty, LANES>
285 where
286 LaneCount<LANES>: SupportedLaneCount,
287 Simd<$unsigned, LANES>:
288 WideningMultiply<Output = (Simd<$unsigned, LANES>, Simd<$unsigned, LANES>)>,
289 StandardUniform: Distribution<Simd<$unsigned, LANES>>,
290 {
291 type Sampler = UniformInt<Simd<$ty, LANES>>;
292 }
293
294 #[cfg(feature = "simd_support")]
295 impl<const LANES: usize> UniformSampler for UniformInt<Simd<$ty, LANES>>
296 where
297 LaneCount<LANES>: SupportedLaneCount,
298 Simd<$unsigned, LANES>:
299 WideningMultiply<Output = (Simd<$unsigned, LANES>, Simd<$unsigned, LANES>)>,
300 StandardUniform: Distribution<Simd<$unsigned, LANES>>,
301 {
302 type X = Simd<$ty, LANES>;
303
304 #[inline] // if the range is constant, this helps LLVM to do the
305 // calculations at compile-time.
306 fn new<B1, B2>(low_b: B1, high_b: B2) -> Result<Self, Error>
307 where B1: SampleBorrow<Self::X> + Sized,
308 B2: SampleBorrow<Self::X> + Sized
309 {
310 let low = *low_b.borrow();
311 let high = *high_b.borrow();
312 if !(low.simd_lt(high).all()) {
313 return Err(Error::EmptyRange);
314 }
315 UniformSampler::new_inclusive(low, high - Simd::splat(1))
316 }
317
318 #[inline] // if the range is constant, this helps LLVM to do the
319 // calculations at compile-time.
320 fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Result<Self, Error>
321 where B1: SampleBorrow<Self::X> + Sized,
322 B2: SampleBorrow<Self::X> + Sized
323 {
324 let low = *low_b.borrow();
325 let high = *high_b.borrow();
326 if !(low.simd_le(high).all()) {
327 return Err(Error::EmptyRange);
328 }
329
330 // NOTE: all `Simd` operations are inherently wrapping,
331 // see https://doc.rust-lang.org/std/simd/struct.Simd.html
332 let range: Simd<$unsigned, LANES> = ((high - low) + Simd::splat(1)).cast();
333
334 // We must avoid divide-by-zero by using 0 % 1 == 0.
335 let not_full_range = range.simd_gt(Simd::splat(0));
336 let modulo = not_full_range.select(range, Simd::splat(1));
337 let ints_to_reject = range.wrapping_neg() % modulo;
338
339 Ok(UniformInt {
340 low,
341 // These are really $unsigned values, but store as $ty:
342 range: range.cast(),
343 thresh: ints_to_reject.cast(),
344 })
345 }
346
347 fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
348 let range: Simd<$unsigned, LANES> = self.range.cast();
349 let thresh: Simd<$unsigned, LANES> = self.thresh.cast();
350
351 // This might seem very slow, generating a whole new
352 // SIMD vector for every sample rejection. For most uses
353 // though, the chance of rejection is small and provides good
354 // general performance. With multiple lanes, that chance is
355 // multiplied. To mitigate this, we replace only the lanes of
356 // the vector which fail, iteratively reducing the chance of
357 // rejection. The replacement method does however add a little
358 // overhead. Benchmarking or calculating probabilities might
359 // reveal contexts where this replacement method is slower.
360 let mut v: Simd<$unsigned, LANES> = rng.random();
361 loop {
362 let (hi, lo) = v.wmul(range);
363 let mask = lo.simd_ge(thresh);
364 if mask.all() {
365 let hi: Simd<$ty, LANES> = hi.cast();
366 // wrapping addition
367 let result = self.low + hi;
368 // `select` here compiles to a blend operation
369 // When `range.eq(0).none()` the compare and blend
370 // operations are avoided.
371 let v: Simd<$ty, LANES> = v.cast();
372 return range.simd_gt(Simd::splat(0)).select(result, v);
373 }
374 // Replace only the failing lanes
375 v = mask.select(v, rng.random());
376 }
377 }
378 }
379 };
380
381 // bulk implementation
382 ($(($unsigned:ident, $signed:ident)),+) => {
383 $(
384 uniform_simd_int_impl!($unsigned, $unsigned);
385 uniform_simd_int_impl!($signed, $unsigned);
386 )+
387 };
388}
389
390#[cfg(feature = "simd_support")]
391uniform_simd_int_impl! { (u8, i8), (u16, i16), (u32, i32), (u64, i64) }
392
393/// The back-end implementing [`UniformSampler`] for `usize`.
394///
395/// # Implementation notes
396///
397/// Sampling a `usize` value is usually used in relation to the length of an
398/// array or other memory structure, thus it is reasonable to assume that the
399/// vast majority of use-cases will have a maximum size under [`u32::MAX`].
400/// In part to optimise for this use-case, but mostly to ensure that results
401/// are portable across 32-bit and 64-bit architectures (as far as is possible),
402/// this implementation will use 32-bit sampling when possible.
403#[cfg(any(target_pointer_width = "32", target_pointer_width = "64"))]
404#[derive(Clone, Copy, Debug, PartialEq, Eq)]
405pub struct UniformUsize {
406 low: usize,
407 range: usize,
408 thresh: usize,
409 #[cfg(target_pointer_width = "64")]
410 mode64: bool,
411}
412
413#[cfg(any(target_pointer_width = "32", target_pointer_width = "64"))]
414impl SampleUniform for usize {
415 type Sampler = UniformUsize;
416}
417
418#[cfg(any(target_pointer_width = "32", target_pointer_width = "64"))]
419impl UniformSampler for UniformUsize {
420 type X = usize;
421
422 #[inline] // if the range is constant, this helps LLVM to do the
423 // calculations at compile-time.
424 fn new<B1, B2>(low_b: B1, high_b: B2) -> Result<Self, Error>
425 where
426 B1: SampleBorrow<Self::X> + Sized,
427 B2: SampleBorrow<Self::X> + Sized,
428 {
429 let low = *low_b.borrow();
430 let high = *high_b.borrow();
431 if !(low < high) {
432 return Err(Error::EmptyRange);
433 }
434
435 UniformSampler::new_inclusive(low, high - 1)
436 }
437
438 #[inline] // if the range is constant, this helps LLVM to do the
439 // calculations at compile-time.
440 fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Result<Self, Error>
441 where
442 B1: SampleBorrow<Self::X> + Sized,
443 B2: SampleBorrow<Self::X> + Sized,
444 {
445 let low = *low_b.borrow();
446 let high = *high_b.borrow();
447 if !(low <= high) {
448 return Err(Error::EmptyRange);
449 }
450
451 #[cfg(target_pointer_width = "64")]
452 let mode64 = high > (u32::MAX as usize);
453 #[cfg(target_pointer_width = "32")]
454 let mode64 = false;
455
456 let (range, thresh);
457 if cfg!(target_pointer_width = "64") && !mode64 {
458 let range32 = (high as u32).wrapping_sub(low as u32).wrapping_add(1);
459 range = range32 as usize;
460 thresh = if range32 > 0 {
461 (range32.wrapping_neg() % range32) as usize
462 } else {
463 0
464 };
465 } else {
466 range = high.wrapping_sub(low).wrapping_add(1);
467 thresh = if range > 0 {
468 range.wrapping_neg() % range
469 } else {
470 0
471 };
472 }
473
474 Ok(UniformUsize {
475 low,
476 range,
477 thresh,
478 #[cfg(target_pointer_width = "64")]
479 mode64,
480 })
481 }
482
483 #[inline]
484 fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
485 #[cfg(target_pointer_width = "32")]
486 let mode32 = true;
487 #[cfg(target_pointer_width = "64")]
488 let mode32 = !self.mode64;
489
490 if mode32 {
491 let range = self.range as u32;
492 if range == 0 {
493 return rng.random::<u32>() as usize;
494 }
495
496 let thresh = self.thresh as u32;
497 let hi = loop {
498 let (hi, lo) = rng.random::<u32>().wmul(range);
499 if lo >= thresh {
500 break hi;
501 }
502 };
503 self.low.wrapping_add(hi as usize)
504 } else {
505 let range = self.range as u64;
506 if range == 0 {
507 return rng.random::<u64>() as usize;
508 }
509
510 let thresh = self.thresh as u64;
511 let hi = loop {
512 let (hi, lo) = rng.random::<u64>().wmul(range);
513 if lo >= thresh {
514 break hi;
515 }
516 };
517 self.low.wrapping_add(hi as usize)
518 }
519 }
520
521 #[inline]
522 fn sample_single<R: Rng + ?Sized, B1, B2>(
523 low_b: B1,
524 high_b: B2,
525 rng: &mut R,
526 ) -> Result<Self::X, Error>
527 where
528 B1: SampleBorrow<Self::X> + Sized,
529 B2: SampleBorrow<Self::X> + Sized,
530 {
531 let low = *low_b.borrow();
532 let high = *high_b.borrow();
533 if !(low < high) {
534 return Err(Error::EmptyRange);
535 }
536
537 if cfg!(target_pointer_width = "64") && high > (u32::MAX as usize) {
538 return UniformInt::<u64>::sample_single(low as u64, high as u64, rng)
539 .map(|x| x as usize);
540 }
541
542 UniformInt::<u32>::sample_single(low as u32, high as u32, rng).map(|x| x as usize)
543 }
544
545 #[inline]
546 fn sample_single_inclusive<R: Rng + ?Sized, B1, B2>(
547 low_b: B1,
548 high_b: B2,
549 rng: &mut R,
550 ) -> Result<Self::X, Error>
551 where
552 B1: SampleBorrow<Self::X> + Sized,
553 B2: SampleBorrow<Self::X> + Sized,
554 {
555 let low = *low_b.borrow();
556 let high = *high_b.borrow();
557 if !(low <= high) {
558 return Err(Error::EmptyRange);
559 }
560
561 if cfg!(target_pointer_width = "64") && high > (u32::MAX as usize) {
562 return UniformInt::<u64>::sample_single_inclusive(low as u64, high as u64, rng)
563 .map(|x| x as usize);
564 }
565
566 UniformInt::<u32>::sample_single_inclusive(low as u32, high as u32, rng).map(|x| x as usize)
567 }
568}
569
570#[cfg(test)]
571mod tests {
572 use super::*;
573 use crate::distr::{Distribution, Uniform};
574 use core::fmt::Debug;
575 use core::ops::Add;
576
577 #[test]
578 fn test_uniform_bad_limits_equal_int() {
579 assert_eq!(Uniform::new(10, 10), Err(Error::EmptyRange));
580 }
581
582 #[test]
583 fn test_uniform_good_limits_equal_int() {
584 let mut rng = crate::test::rng(804);
585 let dist = Uniform::new_inclusive(10, 10).unwrap();
586 for _ in 0..20 {
587 assert_eq!(rng.sample(dist), 10);
588 }
589 }
590
591 #[test]
592 fn test_uniform_bad_limits_flipped_int() {
593 assert_eq!(Uniform::new(10, 5), Err(Error::EmptyRange));
594 }
595
596 #[test]
597 #[cfg_attr(miri, ignore)] // Miri is too slow
598 fn test_integers() {
599 let mut rng = crate::test::rng(251);
600 macro_rules! t {
601 ($ty:ident, $v:expr, $le:expr, $lt:expr) => {{
602 for &(low, high) in $v.iter() {
603 let my_uniform = Uniform::new(low, high).unwrap();
604 for _ in 0..1000 {
605 let v: $ty = rng.sample(my_uniform);
606 assert!($le(low, v) && $lt(v, high));
607 }
608
609 let my_uniform = Uniform::new_inclusive(low, high).unwrap();
610 for _ in 0..1000 {
611 let v: $ty = rng.sample(my_uniform);
612 assert!($le(low, v) && $le(v, high));
613 }
614
615 let my_uniform = Uniform::new(&low, high).unwrap();
616 for _ in 0..1000 {
617 let v: $ty = rng.sample(my_uniform);
618 assert!($le(low, v) && $lt(v, high));
619 }
620
621 let my_uniform = Uniform::new_inclusive(&low, &high).unwrap();
622 for _ in 0..1000 {
623 let v: $ty = rng.sample(my_uniform);
624 assert!($le(low, v) && $le(v, high));
625 }
626
627 for _ in 0..1000 {
628 let v = <$ty as SampleUniform>::Sampler::sample_single(low, high, &mut rng).unwrap();
629 assert!($le(low, v) && $lt(v, high));
630 }
631
632 for _ in 0..1000 {
633 let v = <$ty as SampleUniform>::Sampler::sample_single_inclusive(low, high, &mut rng).unwrap();
634 assert!($le(low, v) && $le(v, high));
635 }
636 }
637 }};
638
639 // scalar bulk
640 ($($ty:ident),*) => {{
641 $(t!(
642 $ty,
643 [(0, 10), (10, 127), ($ty::MIN, $ty::MAX)],
644 |x, y| x <= y,
645 |x, y| x < y
646 );)*
647 }};
648
649 // simd bulk
650 ($($ty:ident),* => $scalar:ident) => {{
651 $(t!(
652 $ty,
653 [
654 ($ty::splat(0), $ty::splat(10)),
655 ($ty::splat(10), $ty::splat(127)),
656 ($ty::splat($scalar::MIN), $ty::splat($scalar::MAX)),
657 ],
658 |x: $ty, y| x.simd_le(y).all(),
659 |x: $ty, y| x.simd_lt(y).all()
660 );)*
661 }};
662 }
663 t!(i8, i16, i32, i64, i128, u8, u16, u32, u64, usize, u128);
664
665 #[cfg(feature = "simd_support")]
666 {
667 t!(u8x4, u8x8, u8x16, u8x32, u8x64 => u8);
668 t!(i8x4, i8x8, i8x16, i8x32, i8x64 => i8);
669 t!(u16x2, u16x4, u16x8, u16x16, u16x32 => u16);
670 t!(i16x2, i16x4, i16x8, i16x16, i16x32 => i16);
671 t!(u32x2, u32x4, u32x8, u32x16 => u32);
672 t!(i32x2, i32x4, i32x8, i32x16 => i32);
673 t!(u64x2, u64x4, u64x8 => u64);
674 t!(i64x2, i64x4, i64x8 => i64);
675 }
676 }
677
678 #[test]
679 fn test_uniform_from_std_range() {
680 let r = Uniform::try_from(2u32..7).unwrap();
681 assert_eq!(r.0.low, 2);
682 assert_eq!(r.0.range, 5);
683 }
684
685 #[test]
686 fn test_uniform_from_std_range_bad_limits() {
687 #![allow(clippy::reversed_empty_ranges)]
688 assert!(Uniform::try_from(100..10).is_err());
689 assert!(Uniform::try_from(100..100).is_err());
690 }
691
692 #[test]
693 fn test_uniform_from_std_range_inclusive() {
694 let r = Uniform::try_from(2u32..=6).unwrap();
695 assert_eq!(r.0.low, 2);
696 assert_eq!(r.0.range, 5);
697 }
698
699 #[test]
700 fn test_uniform_from_std_range_inclusive_bad_limits() {
701 #![allow(clippy::reversed_empty_ranges)]
702 assert!(Uniform::try_from(100..=10).is_err());
703 assert!(Uniform::try_from(100..=99).is_err());
704 }
705
706 #[test]
707 fn value_stability() {
708 fn test_samples<T: SampleUniform + Copy + Debug + PartialEq + Add<T>>(
709 lb: T,
710 ub: T,
711 ub_excl: T,
712 expected: &[T],
713 ) where
714 Uniform<T>: Distribution<T>,
715 {
716 let mut rng = crate::test::rng(897);
717 let mut buf = [lb; 6];
718
719 for x in &mut buf[0..3] {
720 *x = T::Sampler::sample_single_inclusive(lb, ub, &mut rng).unwrap();
721 }
722
723 let distr = Uniform::new_inclusive(lb, ub).unwrap();
724 for x in &mut buf[3..6] {
725 *x = rng.sample(&distr);
726 }
727 assert_eq!(&buf, expected);
728
729 let mut rng = crate::test::rng(897);
730
731 for x in &mut buf[0..3] {
732 *x = T::Sampler::sample_single(lb, ub_excl, &mut rng).unwrap();
733 }
734
735 let distr = Uniform::new(lb, ub_excl).unwrap();
736 for x in &mut buf[3..6] {
737 *x = rng.sample(&distr);
738 }
739 assert_eq!(&buf, expected);
740 }
741
742 test_samples(-105i8, 111, 112, &[-99, -48, 107, 72, -19, 56]);
743 test_samples(2i16, 1352, 1353, &[43, 361, 1325, 1109, 539, 1005]);
744 test_samples(
745 -313853i32,
746 13513,
747 13514,
748 &[-303803, -226673, 6912, -45605, -183505, -70668],
749 );
750 test_samples(
751 131521i64,
752 6542165,
753 6542166,
754 &[1838724, 5384489, 4893692, 3712948, 3951509, 4094926],
755 );
756 test_samples(
757 -0x8000_0000_0000_0000_0000_0000_0000_0000i128,
758 -1,
759 0,
760 &[
761 -30725222750250982319765550926688025855,
762 -75088619368053423329503924805178012357,
763 -64950748766625548510467638647674468829,
764 -41794017901603587121582892414659436495,
765 -63623852319608406524605295913876414006,
766 -17404679390297612013597359206379189023,
767 ],
768 );
769 test_samples(11u8, 218, 219, &[17, 66, 214, 181, 93, 165]);
770 test_samples(11u16, 218, 219, &[17, 66, 214, 181, 93, 165]);
771 test_samples(11u32, 218, 219, &[17, 66, 214, 181, 93, 165]);
772 test_samples(11u64, 218, 219, &[66, 181, 165, 127, 134, 139]);
773 test_samples(11u128, 218, 219, &[181, 127, 139, 167, 141, 197]);
774 test_samples(11usize, 218, 219, &[17, 66, 214, 181, 93, 165]);
775
776 #[cfg(feature = "simd_support")]
777 {
778 let lb = Simd::from([11u8, 0, 128, 127]);
779 let ub = Simd::from([218, 254, 254, 254]);
780 let ub_excl = ub + Simd::splat(1);
781 test_samples(
782 lb,
783 ub,
784 ub_excl,
785 &[
786 Simd::from([13, 5, 237, 130]),
787 Simd::from([126, 186, 149, 161]),
788 Simd::from([103, 86, 234, 252]),
789 Simd::from([35, 18, 225, 231]),
790 Simd::from([106, 153, 246, 177]),
791 Simd::from([195, 168, 149, 222]),
792 ],
793 );
794 }
795 }
796}
797