rand/distributions/
uniform.rs

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)` can sample integers from 1 to 6, like a
14//! standard die. [`Rng::gen_range`] supports any type supported by
15//! [`Uniform`].
16//!
17//! This distribution is provided with support for several primitive types
18//! (all integer and floating-point types) as well as [`std::time::Duration`],
19//! and supports extension to user-defined types via a type-specific *back-end*
20//! implementation.
21//!
22//! The types [`UniformInt`], [`UniformFloat`] and [`UniformDuration`] are the
23//! back-ends supporting sampling from primitive integer and floating-point
24//! ranges as well as from [`std::time::Duration`]; these types do not normally
25//! need to be used directly (unless implementing a derived back-end).
26//!
27//! # Example usage
28//!
29//! ```
30//! use rand::{Rng, thread_rng};
31//! use rand::distributions::Uniform;
32//!
33//! let mut rng = thread_rng();
34//! let side = Uniform::new(-10.0, 10.0);
35//!
36//! // sample between 1 and 10 points
37//! for _ in 0..rng.gen_range(1..=10) {
38//!     // sample a point from the square with sides -10 - 10 in two dimensions
39//!     let (x, y) = (rng.sample(side), rng.sample(side));
40//!     println!("Point: {}, {}", x, y);
41//! }
42//! ```
43//!
44//! # Extending `Uniform` to support a custom type
45//!
46//! To extend [`Uniform`] to support your own types, write a back-end which
47//! implements the [`UniformSampler`] trait, then implement the [`SampleUniform`]
48//! helper trait to "register" your back-end. See the `MyF32` example below.
49//!
50//! At a minimum, the back-end needs to store any parameters needed for sampling
51//! (e.g. the target range) and implement `new`, `new_inclusive` and `sample`.
52//! Those methods should include an assert to check the range is valid (i.e.
53//! `low < high`). The example below merely wraps another back-end.
54//!
55//! The `new`, `new_inclusive` and `sample_single` functions use arguments of
56//! type `SampleBorrow<X>` in order to support passing in values by reference or
57//! by value. In the implementation of these functions, you can choose to
58//! simply use the reference returned by [`SampleBorrow::borrow`], or you can choose
59//! to copy or clone the value, whatever is appropriate for your type.
60//!
61//! ```
62//! use rand::prelude::*;
63//! use rand::distributions::uniform::{Uniform, SampleUniform,
64//!         UniformSampler, UniformFloat, SampleBorrow};
65//!
66//! struct MyF32(f32);
67//!
68//! #[derive(Clone, Copy, Debug)]
69//! struct UniformMyF32(UniformFloat<f32>);
70//!
71//! impl UniformSampler for UniformMyF32 {
72//!     type X = MyF32;
73//!     fn new<B1, B2>(low: B1, high: B2) -> Self
74//!         where B1: SampleBorrow<Self::X> + Sized,
75//!               B2: SampleBorrow<Self::X> + Sized
76//!     {
77//!         UniformMyF32(UniformFloat::<f32>::new(low.borrow().0, high.borrow().0))
78//!     }
79//!     fn new_inclusive<B1, B2>(low: B1, high: B2) -> Self
80//!         where B1: SampleBorrow<Self::X> + Sized,
81//!               B2: SampleBorrow<Self::X> + Sized
82//!     {
83//!         UniformMyF32(UniformFloat::<f32>::new_inclusive(
84//!             low.borrow().0,
85//!             high.borrow().0,
86//!         ))
87//!     }
88//!     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
89//!         MyF32(self.0.sample(rng))
90//!     }
91//! }
92//!
93//! impl SampleUniform for MyF32 {
94//!     type Sampler = UniformMyF32;
95//! }
96//!
97//! let (low, high) = (MyF32(17.0f32), MyF32(22.0f32));
98//! let uniform = Uniform::new(low, high);
99//! let x = uniform.sample(&mut thread_rng());
100//! ```
101//!
102//! [`SampleUniform`]: crate::distributions::uniform::SampleUniform
103//! [`UniformSampler`]: crate::distributions::uniform::UniformSampler
104//! [`UniformInt`]: crate::distributions::uniform::UniformInt
105//! [`UniformFloat`]: crate::distributions::uniform::UniformFloat
106//! [`UniformDuration`]: crate::distributions::uniform::UniformDuration
107//! [`SampleBorrow::borrow`]: crate::distributions::uniform::SampleBorrow::borrow
108
109use core::time::Duration;
110use core::ops::{Range, RangeInclusive};
111
112use crate::distributions::float::IntoFloat;
113use crate::distributions::utils::{BoolAsSIMD, FloatAsSIMD, FloatSIMDUtils, WideningMultiply};
114use crate::distributions::Distribution;
115use crate::{Rng, RngCore};
116
117#[cfg(not(feature = "std"))]
118#[allow(unused_imports)] // rustc doesn't detect that this is actually used
119use crate::distributions::utils::Float;
120
121#[cfg(feature = "serde1")]
122use serde::{Serialize, Deserialize};
123
124/// Sample values uniformly between two bounds.
125///
126/// [`Uniform::new`] and [`Uniform::new_inclusive`] construct a uniform
127/// distribution sampling from the given range; these functions may do extra
128/// work up front to make sampling of multiple values faster. If only one sample
129/// from the range is required, [`Rng::gen_range`] can be more efficient.
130///
131/// When sampling from a constant range, many calculations can happen at
132/// compile-time and all methods should be fast; for floating-point ranges and
133/// the full range of integer types this should have comparable performance to
134/// the `Standard` distribution.
135///
136/// Steps are taken to avoid bias which might be present in naive
137/// implementations; for example `rng.gen::<u8>() % 170` samples from the range
138/// `[0, 169]` but is twice as likely to select numbers less than 85 than other
139/// values. Further, the implementations here give more weight to the high-bits
140/// generated by the RNG than the low bits, since with some RNGs the low-bits
141/// are of lower quality than the high bits.
142///
143/// Implementations must sample in `[low, high)` range for
144/// `Uniform::new(low, high)`, i.e., excluding `high`. In particular, care must
145/// be taken to ensure that rounding never results values `< low` or `>= high`.
146///
147/// # Example
148///
149/// ```
150/// use rand::distributions::{Distribution, Uniform};
151///
152/// let between = Uniform::from(10..10000);
153/// let mut rng = rand::thread_rng();
154/// let mut sum = 0;
155/// for _ in 0..1000 {
156///     sum += between.sample(&mut rng);
157/// }
158/// println!("{}", sum);
159/// ```
160///
161/// For a single sample, [`Rng::gen_range`] may be preferred:
162///
163/// ```
164/// use rand::Rng;
165///
166/// let mut rng = rand::thread_rng();
167/// println!("{}", rng.gen_range(0..10));
168/// ```
169///
170/// [`new`]: Uniform::new
171/// [`new_inclusive`]: Uniform::new_inclusive
172/// [`Rng::gen_range`]: Rng::gen_range
173#[derive(Clone, Copy, Debug, PartialEq)]
174#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
175#[cfg_attr(feature = "serde1", serde(bound(serialize = "X::Sampler: Serialize")))]
176#[cfg_attr(feature = "serde1", serde(bound(deserialize = "X::Sampler: Deserialize<'de>")))]
177pub struct Uniform<X: SampleUniform>(X::Sampler);
178
179impl<X: SampleUniform> Uniform<X> {
180    /// Create a new `Uniform` instance which samples uniformly from the half
181    /// open range `[low, high)` (excluding `high`). Panics if `low >= high`.
182    pub fn new<B1, B2>(low: B1, high: B2) -> Uniform<X>
183    where
184        B1: SampleBorrow<X> + Sized,
185        B2: SampleBorrow<X> + Sized,
186    {
187        Uniform(X::Sampler::new(low, high))
188    }
189
190    /// Create a new `Uniform` instance which samples uniformly from the closed
191    /// range `[low, high]` (inclusive). Panics if `low > high`.
192    pub fn new_inclusive<B1, B2>(low: B1, high: B2) -> Uniform<X>
193    where
194        B1: SampleBorrow<X> + Sized,
195        B2: SampleBorrow<X> + Sized,
196    {
197        Uniform(X::Sampler::new_inclusive(low, high))
198    }
199}
200
201impl<X: SampleUniform> Distribution<X> for Uniform<X> {
202    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> X {
203        self.0.sample(rng)
204    }
205}
206
207/// Helper trait for creating objects using the correct implementation of
208/// [`UniformSampler`] for the sampling type.
209///
210/// See the [module documentation] on how to implement [`Uniform`] range
211/// sampling for a custom type.
212///
213/// [module documentation]: crate::distributions::uniform
214pub trait SampleUniform: Sized {
215    /// The `UniformSampler` implementation supporting type `X`.
216    type Sampler: UniformSampler<X = Self>;
217}
218
219/// Helper trait handling actual uniform sampling.
220///
221/// See the [module documentation] on how to implement [`Uniform`] range
222/// sampling for a custom type.
223///
224/// Implementation of [`sample_single`] is optional, and is only useful when
225/// the implementation can be faster than `Self::new(low, high).sample(rng)`.
226///
227/// [module documentation]: crate::distributions::uniform
228/// [`sample_single`]: UniformSampler::sample_single
229pub trait UniformSampler: Sized {
230    /// The type sampled by this implementation.
231    type X;
232
233    /// Construct self, with inclusive lower bound and exclusive upper bound
234    /// `[low, high)`.
235    ///
236    /// Usually users should not call this directly but instead use
237    /// `Uniform::new`, which asserts that `low < high` before calling this.
238    fn new<B1, B2>(low: B1, high: B2) -> Self
239    where
240        B1: SampleBorrow<Self::X> + Sized,
241        B2: SampleBorrow<Self::X> + Sized;
242
243    /// Construct self, with inclusive bounds `[low, high]`.
244    ///
245    /// Usually users should not call this directly but instead use
246    /// `Uniform::new_inclusive`, which asserts that `low <= high` before
247    /// calling this.
248    fn new_inclusive<B1, B2>(low: B1, high: B2) -> Self
249    where
250        B1: SampleBorrow<Self::X> + Sized,
251        B2: SampleBorrow<Self::X> + Sized;
252
253    /// Sample a value.
254    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X;
255
256    /// Sample a single value uniformly from a range with inclusive lower bound
257    /// and exclusive upper bound `[low, high)`.
258    ///
259    /// By default this is implemented using
260    /// `UniformSampler::new(low, high).sample(rng)`. However, for some types
261    /// more optimal implementations for single usage may be provided via this
262    /// method (which is the case for integers and floats).
263    /// Results may not be identical.
264    ///
265    /// Note that to use this method in a generic context, the type needs to be
266    /// retrieved via `SampleUniform::Sampler` as follows:
267    /// ```
268    /// use rand::{thread_rng, distributions::uniform::{SampleUniform, UniformSampler}};
269    /// # #[allow(unused)]
270    /// fn sample_from_range<T: SampleUniform>(lb: T, ub: T) -> T {
271    ///     let mut rng = thread_rng();
272    ///     <T as SampleUniform>::Sampler::sample_single(lb, ub, &mut rng)
273    /// }
274    /// ```
275    fn sample_single<R: Rng + ?Sized, B1, B2>(low: B1, high: B2, rng: &mut R) -> Self::X
276    where
277        B1: SampleBorrow<Self::X> + Sized,
278        B2: SampleBorrow<Self::X> + Sized,
279    {
280        let uniform: Self = UniformSampler::new(low, high);
281        uniform.sample(rng)
282    }
283
284    /// Sample a single value uniformly from a range with inclusive lower bound
285    /// and inclusive upper bound `[low, high]`.
286    ///
287    /// By default this is implemented using
288    /// `UniformSampler::new_inclusive(low, high).sample(rng)`. However, for
289    /// some types more optimal implementations for single usage may be provided
290    /// via this method.
291    /// Results may not be identical.
292    fn sample_single_inclusive<R: Rng + ?Sized, B1, B2>(low: B1, high: B2, rng: &mut R)
293        -> Self::X
294        where B1: SampleBorrow<Self::X> + Sized,
295              B2: SampleBorrow<Self::X> + Sized
296    {
297        let uniform: Self = UniformSampler::new_inclusive(low, high);
298        uniform.sample(rng)
299    }
300}
301
302impl<X: SampleUniform> From<Range<X>> for Uniform<X> {
303    fn from(r: ::core::ops::Range<X>) -> Uniform<X> {
304        Uniform::new(r.start, r.end)
305    }
306}
307
308impl<X: SampleUniform> From<RangeInclusive<X>> for Uniform<X> {
309    fn from(r: ::core::ops::RangeInclusive<X>) -> Uniform<X> {
310        Uniform::new_inclusive(r.start(), r.end())
311    }
312}
313
314
315/// Helper trait similar to [`Borrow`] but implemented
316/// only for SampleUniform and references to SampleUniform in
317/// order to resolve ambiguity issues.
318///
319/// [`Borrow`]: std::borrow::Borrow
320pub trait SampleBorrow<Borrowed> {
321    /// Immutably borrows from an owned value. See [`Borrow::borrow`]
322    ///
323    /// [`Borrow::borrow`]: std::borrow::Borrow::borrow
324    fn borrow(&self) -> &Borrowed;
325}
326impl<Borrowed> SampleBorrow<Borrowed> for Borrowed
327where Borrowed: SampleUniform
328{
329    #[inline(always)]
330    fn borrow(&self) -> &Borrowed {
331        self
332    }
333}
334impl<'a, Borrowed> SampleBorrow<Borrowed> for &'a Borrowed
335where Borrowed: SampleUniform
336{
337    #[inline(always)]
338    fn borrow(&self) -> &Borrowed {
339        *self
340    }
341}
342
343/// Range that supports generating a single sample efficiently.
344///
345/// Any type implementing this trait can be used to specify the sampled range
346/// for `Rng::gen_range`.
347pub trait SampleRange<T> {
348    /// Generate a sample from the given range.
349    fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> T;
350
351    /// Check whether the range is empty.
352    fn is_empty(&self) -> bool;
353}
354
355impl<T: SampleUniform + PartialOrd> SampleRange<T> for Range<T> {
356    #[inline]
357    fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> T {
358        T::Sampler::sample_single(self.start, self.end, rng)
359    }
360
361    #[inline]
362    fn is_empty(&self) -> bool {
363        !(self.start < self.end)
364    }
365}
366
367impl<T: SampleUniform + PartialOrd> SampleRange<T> for RangeInclusive<T> {
368    #[inline]
369    fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> T {
370        T::Sampler::sample_single_inclusive(self.start(), self.end(), rng)
371    }
372
373    #[inline]
374    fn is_empty(&self) -> bool {
375        !(self.start() <= self.end())
376    }
377}
378
379
380////////////////////////////////////////////////////////////////////////////////
381
382// What follows are all back-ends.
383
384
385/// The back-end implementing [`UniformSampler`] for integer types.
386///
387/// Unless you are implementing [`UniformSampler`] for your own type, this type
388/// should not be used directly, use [`Uniform`] instead.
389///
390/// # Implementation notes
391///
392/// For simplicity, we use the same generic struct `UniformInt<X>` for all
393/// integer types `X`. This gives us only one field type, `X`; to store unsigned
394/// values of this size, we take use the fact that these conversions are no-ops.
395///
396/// For a closed range, the number of possible numbers we should generate is
397/// `range = (high - low + 1)`. To avoid bias, we must ensure that the size of
398/// our sample space, `zone`, is a multiple of `range`; other values must be
399/// rejected (by replacing with a new random sample).
400///
401/// As a special case, we use `range = 0` to represent the full range of the
402/// result type (i.e. for `new_inclusive($ty::MIN, $ty::MAX)`).
403///
404/// The optimum `zone` is the largest product of `range` which fits in our
405/// (unsigned) target type. We calculate this by calculating how many numbers we
406/// must reject: `reject = (MAX + 1) % range = (MAX - range + 1) % range`. Any (large)
407/// product of `range` will suffice, thus in `sample_single` we multiply by a
408/// power of 2 via bit-shifting (faster but may cause more rejections).
409///
410/// The smallest integer PRNGs generate is `u32`. For 8- and 16-bit outputs we
411/// use `u32` for our `zone` and samples (because it's not slower and because
412/// it reduces the chance of having to reject a sample). In this case we cannot
413/// store `zone` in the target type since it is too large, however we know
414/// `ints_to_reject < range <= $unsigned::MAX`.
415///
416/// An alternative to using a modulus is widening multiply: After a widening
417/// multiply by `range`, the result is in the high word. Then comparing the low
418/// word against `zone` makes sure our distribution is uniform.
419#[derive(Clone, Copy, Debug, PartialEq)]
420#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
421pub struct UniformInt<X> {
422    low: X,
423    range: X,
424    z: X, // either ints_to_reject or zone depending on implementation
425}
426
427macro_rules! uniform_int_impl {
428    ($ty:ty, $unsigned:ident, $u_large:ident) => {
429        impl SampleUniform for $ty {
430            type Sampler = UniformInt<$ty>;
431        }
432
433        impl UniformSampler for UniformInt<$ty> {
434            // We play free and fast with unsigned vs signed here
435            // (when $ty is signed), but that's fine, since the
436            // contract of this macro is for $ty and $unsigned to be
437            // "bit-equal", so casting between them is a no-op.
438
439            type X = $ty;
440
441            #[inline] // if the range is constant, this helps LLVM to do the
442                      // calculations at compile-time.
443            fn new<B1, B2>(low_b: B1, high_b: B2) -> Self
444            where
445                B1: SampleBorrow<Self::X> + Sized,
446                B2: SampleBorrow<Self::X> + Sized,
447            {
448                let low = *low_b.borrow();
449                let high = *high_b.borrow();
450                assert!(low < high, "Uniform::new called with `low >= high`");
451                UniformSampler::new_inclusive(low, high - 1)
452            }
453
454            #[inline] // if the range is constant, this helps LLVM to do the
455                      // calculations at compile-time.
456            fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self
457            where
458                B1: SampleBorrow<Self::X> + Sized,
459                B2: SampleBorrow<Self::X> + Sized,
460            {
461                let low = *low_b.borrow();
462                let high = *high_b.borrow();
463                assert!(
464                    low <= high,
465                    "Uniform::new_inclusive called with `low > high`"
466                );
467                let unsigned_max = ::core::$u_large::MAX;
468
469                let range = high.wrapping_sub(low).wrapping_add(1) as $unsigned;
470                let ints_to_reject = if range > 0 {
471                    let range = $u_large::from(range);
472                    (unsigned_max - range + 1) % range
473                } else {
474                    0
475                };
476
477                UniformInt {
478                    low,
479                    // These are really $unsigned values, but store as $ty:
480                    range: range as $ty,
481                    z: ints_to_reject as $unsigned as $ty,
482                }
483            }
484
485            #[inline]
486            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
487                let range = self.range as $unsigned as $u_large;
488                if range > 0 {
489                    let unsigned_max = ::core::$u_large::MAX;
490                    let zone = unsigned_max - (self.z as $unsigned as $u_large);
491                    loop {
492                        let v: $u_large = rng.gen();
493                        let (hi, lo) = v.wmul(range);
494                        if lo <= zone {
495                            return self.low.wrapping_add(hi as $ty);
496                        }
497                    }
498                } else {
499                    // Sample from the entire integer range.
500                    rng.gen()
501                }
502            }
503
504            #[inline]
505            fn sample_single<R: Rng + ?Sized, B1, B2>(low_b: B1, high_b: B2, rng: &mut R) -> Self::X
506            where
507                B1: SampleBorrow<Self::X> + Sized,
508                B2: SampleBorrow<Self::X> + Sized,
509            {
510                let low = *low_b.borrow();
511                let high = *high_b.borrow();
512                assert!(low < high, "UniformSampler::sample_single: low >= high");
513                Self::sample_single_inclusive(low, high - 1, rng)
514            }
515
516            #[inline]
517            fn sample_single_inclusive<R: Rng + ?Sized, B1, B2>(low_b: B1, high_b: B2, rng: &mut R) -> Self::X
518            where
519                B1: SampleBorrow<Self::X> + Sized,
520                B2: SampleBorrow<Self::X> + Sized,
521            {
522                let low = *low_b.borrow();
523                let high = *high_b.borrow();
524                assert!(low <= high, "UniformSampler::sample_single_inclusive: low > high");
525                let range = high.wrapping_sub(low).wrapping_add(1) as $unsigned as $u_large;
526                // If the above resulted in wrap-around to 0, the range is $ty::MIN..=$ty::MAX,
527                // and any integer will do.
528                if range == 0 {
529                    return rng.gen();
530                }
531
532                let zone = if ::core::$unsigned::MAX <= ::core::u16::MAX as $unsigned {
533                    // Using a modulus is faster than the approximation for
534                    // i8 and i16. I suppose we trade the cost of one
535                    // modulus for near-perfect branch prediction.
536                    let unsigned_max: $u_large = ::core::$u_large::MAX;
537                    let ints_to_reject = (unsigned_max - range + 1) % range;
538                    unsigned_max - ints_to_reject
539                } else {
540                    // conservative but fast approximation. `- 1` is necessary to allow the
541                    // same comparison without bias.
542                    (range << range.leading_zeros()).wrapping_sub(1)
543                };
544
545                loop {
546                    let v: $u_large = rng.gen();
547                    let (hi, lo) = v.wmul(range);
548                    if lo <= zone {
549                        return low.wrapping_add(hi as $ty);
550                    }
551                }
552            }
553        }
554    };
555}
556
557uniform_int_impl! { i8, u8, u32 }
558uniform_int_impl! { i16, u16, u32 }
559uniform_int_impl! { i32, u32, u32 }
560uniform_int_impl! { i64, u64, u64 }
561uniform_int_impl! { i128, u128, u128 }
562uniform_int_impl! { isize, usize, usize }
563uniform_int_impl! { u8, u8, u32 }
564uniform_int_impl! { u16, u16, u32 }
565uniform_int_impl! { u32, u32, u32 }
566uniform_int_impl! { u64, u64, u64 }
567uniform_int_impl! { usize, usize, usize }
568uniform_int_impl! { u128, u128, u128 }
569
570impl SampleUniform for char {
571    type Sampler = UniformChar;
572}
573
574/// The back-end implementing [`UniformSampler`] for `char`.
575///
576/// Unless you are implementing [`UniformSampler`] for your own type, this type
577/// should not be used directly, use [`Uniform`] instead.
578///
579/// This differs from integer range sampling since the range `0xD800..=0xDFFF`
580/// are used for surrogate pairs in UCS and UTF-16, and consequently are not
581/// valid Unicode code points. We must therefore avoid sampling values in this
582/// range.
583#[derive(Clone, Copy, Debug)]
584#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
585pub struct UniformChar {
586    sampler: UniformInt<u32>,
587}
588
589/// UTF-16 surrogate range start
590const CHAR_SURROGATE_START: u32 = 0xD800;
591/// UTF-16 surrogate range size
592const CHAR_SURROGATE_LEN: u32 = 0xE000 - CHAR_SURROGATE_START;
593
594/// Convert `char` to compressed `u32`
595fn char_to_comp_u32(c: char) -> u32 {
596    match c as u32 {
597        c if c >= CHAR_SURROGATE_START => c - CHAR_SURROGATE_LEN,
598        c => c,
599    }
600}
601
602impl UniformSampler for UniformChar {
603    type X = char;
604
605    #[inline] // if the range is constant, this helps LLVM to do the
606              // calculations at compile-time.
607    fn new<B1, B2>(low_b: B1, high_b: B2) -> Self
608    where
609        B1: SampleBorrow<Self::X> + Sized,
610        B2: SampleBorrow<Self::X> + Sized,
611    {
612        let low = char_to_comp_u32(*low_b.borrow());
613        let high = char_to_comp_u32(*high_b.borrow());
614        let sampler = UniformInt::<u32>::new(low, high);
615        UniformChar { sampler }
616    }
617
618    #[inline] // if the range is constant, this helps LLVM to do the
619              // calculations at compile-time.
620    fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self
621    where
622        B1: SampleBorrow<Self::X> + Sized,
623        B2: SampleBorrow<Self::X> + Sized,
624    {
625        let low = char_to_comp_u32(*low_b.borrow());
626        let high = char_to_comp_u32(*high_b.borrow());
627        let sampler = UniformInt::<u32>::new_inclusive(low, high);
628        UniformChar { sampler }
629    }
630
631    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
632        let mut x = self.sampler.sample(rng);
633        if x >= CHAR_SURROGATE_START {
634            x += CHAR_SURROGATE_LEN;
635        }
636        // SAFETY: x must not be in surrogate range or greater than char::MAX.
637        // This relies on range constructors which accept char arguments.
638        // Validity of input char values is assumed.
639        unsafe { core::char::from_u32_unchecked(x) }
640    }
641}
642
643/// The back-end implementing [`UniformSampler`] for floating-point types.
644///
645/// Unless you are implementing [`UniformSampler`] for your own type, this type
646/// should not be used directly, use [`Uniform`] instead.
647///
648/// # Implementation notes
649///
650/// Instead of generating a float in the `[0, 1)` range using [`Standard`], the
651/// `UniformFloat` implementation converts the output of an PRNG itself. This
652/// way one or two steps can be optimized out.
653///
654/// The floats are first converted to a value in the `[1, 2)` interval using a
655/// transmute-based method, and then mapped to the expected range with a
656/// multiply and addition. Values produced this way have what equals 23 bits of
657/// random digits for an `f32`, and 52 for an `f64`.
658///
659/// [`new`]: UniformSampler::new
660/// [`new_inclusive`]: UniformSampler::new_inclusive
661/// [`Standard`]: crate::distributions::Standard
662#[derive(Clone, Copy, Debug, PartialEq)]
663#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
664pub struct UniformFloat<X> {
665    low: X,
666    scale: X,
667}
668
669macro_rules! uniform_float_impl {
670    ($ty:ty, $uty:ident, $f_scalar:ident, $u_scalar:ident, $bits_to_discard:expr) => {
671        impl SampleUniform for $ty {
672            type Sampler = UniformFloat<$ty>;
673        }
674
675        impl UniformSampler for UniformFloat<$ty> {
676            type X = $ty;
677
678            fn new<B1, B2>(low_b: B1, high_b: B2) -> Self
679            where
680                B1: SampleBorrow<Self::X> + Sized,
681                B2: SampleBorrow<Self::X> + Sized,
682            {
683                let low = *low_b.borrow();
684                let high = *high_b.borrow();
685                debug_assert!(
686                    low.all_finite(),
687                    "Uniform::new called with `low` non-finite."
688                );
689                debug_assert!(
690                    high.all_finite(),
691                    "Uniform::new called with `high` non-finite."
692                );
693                assert!(low.all_lt(high), "Uniform::new called with `low >= high`");
694                let max_rand = <$ty>::splat(
695                    (::core::$u_scalar::MAX >> $bits_to_discard).into_float_with_exponent(0) - 1.0,
696                );
697
698                let mut scale = high - low;
699                assert!(scale.all_finite(), "Uniform::new: range overflow");
700
701                loop {
702                    let mask = (scale * max_rand + low).ge_mask(high);
703                    if mask.none() {
704                        break;
705                    }
706                    scale = scale.decrease_masked(mask);
707                }
708
709                debug_assert!(<$ty>::splat(0.0).all_le(scale));
710
711                UniformFloat { low, scale }
712            }
713
714            fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self
715            where
716                B1: SampleBorrow<Self::X> + Sized,
717                B2: SampleBorrow<Self::X> + Sized,
718            {
719                let low = *low_b.borrow();
720                let high = *high_b.borrow();
721                debug_assert!(
722                    low.all_finite(),
723                    "Uniform::new_inclusive called with `low` non-finite."
724                );
725                debug_assert!(
726                    high.all_finite(),
727                    "Uniform::new_inclusive called with `high` non-finite."
728                );
729                assert!(
730                    low.all_le(high),
731                    "Uniform::new_inclusive called with `low > high`"
732                );
733                let max_rand = <$ty>::splat(
734                    (::core::$u_scalar::MAX >> $bits_to_discard).into_float_with_exponent(0) - 1.0,
735                );
736
737                let mut scale = (high - low) / max_rand;
738                assert!(scale.all_finite(), "Uniform::new_inclusive: range overflow");
739
740                loop {
741                    let mask = (scale * max_rand + low).gt_mask(high);
742                    if mask.none() {
743                        break;
744                    }
745                    scale = scale.decrease_masked(mask);
746                }
747
748                debug_assert!(<$ty>::splat(0.0).all_le(scale));
749
750                UniformFloat { low, scale }
751            }
752
753            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
754                // Generate a value in the range [1, 2)
755                let value1_2 = (rng.gen::<$uty>() >> $bits_to_discard).into_float_with_exponent(0);
756
757                // Get a value in the range [0, 1) in order to avoid
758                // overflowing into infinity when multiplying with scale
759                let value0_1 = value1_2 - 1.0;
760
761                // We don't use `f64::mul_add`, because it is not available with
762                // `no_std`. Furthermore, it is slower for some targets (but
763                // faster for others). However, the order of multiplication and
764                // addition is important, because on some platforms (e.g. ARM)
765                // it will be optimized to a single (non-FMA) instruction.
766                value0_1 * self.scale + self.low
767            }
768
769            #[inline]
770            fn sample_single<R: Rng + ?Sized, B1, B2>(low_b: B1, high_b: B2, rng: &mut R) -> Self::X
771            where
772                B1: SampleBorrow<Self::X> + Sized,
773                B2: SampleBorrow<Self::X> + Sized,
774            {
775                let low = *low_b.borrow();
776                let high = *high_b.borrow();
777                debug_assert!(
778                    low.all_finite(),
779                    "UniformSampler::sample_single called with `low` non-finite."
780                );
781                debug_assert!(
782                    high.all_finite(),
783                    "UniformSampler::sample_single called with `high` non-finite."
784                );
785                assert!(
786                    low.all_lt(high),
787                    "UniformSampler::sample_single: low >= high"
788                );
789                let mut scale = high - low;
790                assert!(scale.all_finite(), "UniformSampler::sample_single: range overflow");
791
792                loop {
793                    // Generate a value in the range [1, 2)
794                    let value1_2 =
795                        (rng.gen::<$uty>() >> $bits_to_discard).into_float_with_exponent(0);
796
797                    // Get a value in the range [0, 1) in order to avoid
798                    // overflowing into infinity when multiplying with scale
799                    let value0_1 = value1_2 - 1.0;
800
801                    // Doing multiply before addition allows some architectures
802                    // to use a single instruction.
803                    let res = value0_1 * scale + low;
804
805                    debug_assert!(low.all_le(res) || !scale.all_finite());
806                    if res.all_lt(high) {
807                        return res;
808                    }
809
810                    // This handles a number of edge cases.
811                    // * `low` or `high` is NaN. In this case `scale` and
812                    //   `res` are going to end up as NaN.
813                    // * `low` is negative infinity and `high` is finite.
814                    //   `scale` is going to be infinite and `res` will be
815                    //   NaN.
816                    // * `high` is positive infinity and `low` is finite.
817                    //   `scale` is going to be infinite and `res` will
818                    //   be infinite or NaN (if value0_1 is 0).
819                    // * `low` is negative infinity and `high` is positive
820                    //   infinity. `scale` will be infinite and `res` will
821                    //   be NaN.
822                    // * `low` and `high` are finite, but `high - low`
823                    //   overflows to infinite. `scale` will be infinite
824                    //   and `res` will be infinite or NaN (if value0_1 is 0).
825                    // So if `high` or `low` are non-finite, we are guaranteed
826                    // to fail the `res < high` check above and end up here.
827                    //
828                    // While we technically should check for non-finite `low`
829                    // and `high` before entering the loop, by doing the checks
830                    // here instead, we allow the common case to avoid these
831                    // checks. But we are still guaranteed that if `low` or
832                    // `high` are non-finite we'll end up here and can do the
833                    // appropriate checks.
834                    //
835                    // Likewise `high - low` overflowing to infinity is also
836                    // rare, so handle it here after the common case.
837                    let mask = !scale.finite_mask();
838                    if mask.any() {
839                        assert!(
840                            low.all_finite() && high.all_finite(),
841                            "Uniform::sample_single: low and high must be finite"
842                        );
843                        scale = scale.decrease_masked(mask);
844                    }
845                }
846            }
847        }
848    };
849}
850
851uniform_float_impl! { f32, u32, f32, u32, 32 - 23 }
852uniform_float_impl! { f64, u64, f64, u64, 64 - 52 }
853
854/// The back-end implementing [`UniformSampler`] for `Duration`.
855///
856/// Unless you are implementing [`UniformSampler`] for your own types, this type
857/// should not be used directly, use [`Uniform`] instead.
858#[derive(Clone, Copy, Debug)]
859#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
860pub struct UniformDuration {
861    mode: UniformDurationMode,
862    offset: u32,
863}
864
865#[derive(Debug, Copy, Clone)]
866#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
867enum UniformDurationMode {
868    Small {
869        secs: u64,
870        nanos: Uniform<u32>,
871    },
872    Medium {
873        nanos: Uniform<u64>,
874    },
875    Large {
876        max_secs: u64,
877        max_nanos: u32,
878        secs: Uniform<u64>,
879    },
880}
881
882impl SampleUniform for Duration {
883    type Sampler = UniformDuration;
884}
885
886impl UniformSampler for UniformDuration {
887    type X = Duration;
888
889    #[inline]
890    fn new<B1, B2>(low_b: B1, high_b: B2) -> Self
891    where
892        B1: SampleBorrow<Self::X> + Sized,
893        B2: SampleBorrow<Self::X> + Sized,
894    {
895        let low = *low_b.borrow();
896        let high = *high_b.borrow();
897        assert!(low < high, "Uniform::new called with `low >= high`");
898        UniformDuration::new_inclusive(low, high - Duration::new(0, 1))
899    }
900
901    #[inline]
902    fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self
903    where
904        B1: SampleBorrow<Self::X> + Sized,
905        B2: SampleBorrow<Self::X> + Sized,
906    {
907        let low = *low_b.borrow();
908        let high = *high_b.borrow();
909        assert!(
910            low <= high,
911            "Uniform::new_inclusive called with `low > high`"
912        );
913
914        let low_s = low.as_secs();
915        let low_n = low.subsec_nanos();
916        let mut high_s = high.as_secs();
917        let mut high_n = high.subsec_nanos();
918
919        if high_n < low_n {
920            high_s -= 1;
921            high_n += 1_000_000_000;
922        }
923
924        let mode = if low_s == high_s {
925            UniformDurationMode::Small {
926                secs: low_s,
927                nanos: Uniform::new_inclusive(low_n, high_n),
928            }
929        } else {
930            let max = high_s
931                .checked_mul(1_000_000_000)
932                .and_then(|n| n.checked_add(u64::from(high_n)));
933
934            if let Some(higher_bound) = max {
935                let lower_bound = low_s * 1_000_000_000 + u64::from(low_n);
936                UniformDurationMode::Medium {
937                    nanos: Uniform::new_inclusive(lower_bound, higher_bound),
938                }
939            } else {
940                // An offset is applied to simplify generation of nanoseconds
941                let max_nanos = high_n - low_n;
942                UniformDurationMode::Large {
943                    max_secs: high_s,
944                    max_nanos,
945                    secs: Uniform::new_inclusive(low_s, high_s),
946                }
947            }
948        };
949        UniformDuration {
950            mode,
951            offset: low_n,
952        }
953    }
954
955    #[inline]
956    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Duration {
957        match self.mode {
958            UniformDurationMode::Small { secs, nanos } => {
959                let n = nanos.sample(rng);
960                Duration::new(secs, n)
961            }
962            UniformDurationMode::Medium { nanos } => {
963                let nanos = nanos.sample(rng);
964                Duration::new(nanos / 1_000_000_000, (nanos % 1_000_000_000) as u32)
965            }
966            UniformDurationMode::Large {
967                max_secs,
968                max_nanos,
969                secs,
970            } => {
971                // constant folding means this is at least as fast as `Rng::sample(Range)`
972                let nano_range = Uniform::new(0, 1_000_000_000);
973                loop {
974                    let s = secs.sample(rng);
975                    let n = nano_range.sample(rng);
976                    if !(s == max_secs && n > max_nanos) {
977                        let sum = n + self.offset;
978                        break Duration::new(s, sum);
979                    }
980                }
981            }
982        }
983    }
984}
985
986#[cfg(test)]
987mod tests {
988    use super::*;
989    use crate::rngs::mock::StepRng;
990
991    #[test]
992    #[cfg(feature = "serde1")]
993    fn test_serialization_uniform_duration() {
994        let distr = UniformDuration::new(Duration::from_secs(10), Duration::from_secs(60));
995        let de_distr: UniformDuration = bincode::deserialize(&bincode::serialize(&distr).unwrap()).unwrap();
996        assert_eq!(
997            distr.offset, de_distr.offset
998        );
999        match (distr.mode, de_distr.mode) {
1000            (UniformDurationMode::Small {secs: a_secs, nanos: a_nanos}, UniformDurationMode::Small {secs, nanos}) => {
1001                assert_eq!(a_secs, secs);
1002
1003                assert_eq!(a_nanos.0.low, nanos.0.low);
1004                assert_eq!(a_nanos.0.range, nanos.0.range);
1005                assert_eq!(a_nanos.0.z, nanos.0.z);
1006            }
1007            (UniformDurationMode::Medium {nanos: a_nanos} , UniformDurationMode::Medium {nanos}) => {
1008                assert_eq!(a_nanos.0.low, nanos.0.low);
1009                assert_eq!(a_nanos.0.range, nanos.0.range);
1010                assert_eq!(a_nanos.0.z, nanos.0.z);
1011            }
1012            (UniformDurationMode::Large {max_secs:a_max_secs, max_nanos:a_max_nanos, secs:a_secs}, UniformDurationMode::Large {max_secs, max_nanos, secs} ) => {
1013                assert_eq!(a_max_secs, max_secs);
1014                assert_eq!(a_max_nanos, max_nanos);
1015
1016                assert_eq!(a_secs.0.low, secs.0.low);
1017                assert_eq!(a_secs.0.range, secs.0.range);
1018                assert_eq!(a_secs.0.z, secs.0.z);
1019            }
1020            _ => panic!("`UniformDurationMode` was not serialized/deserialized correctly")
1021        }
1022    }
1023    
1024    #[test]
1025    #[cfg(feature = "serde1")]
1026    fn test_uniform_serialization() {
1027        let unit_box: Uniform<i32>  = Uniform::new(-1, 1);
1028        let de_unit_box: Uniform<i32> = bincode::deserialize(&bincode::serialize(&unit_box).unwrap()).unwrap();
1029
1030        assert_eq!(unit_box.0.low, de_unit_box.0.low);
1031        assert_eq!(unit_box.0.range, de_unit_box.0.range);
1032        assert_eq!(unit_box.0.z, de_unit_box.0.z);
1033
1034        let unit_box: Uniform<f32> = Uniform::new(-1., 1.);
1035        let de_unit_box: Uniform<f32> = bincode::deserialize(&bincode::serialize(&unit_box).unwrap()).unwrap();
1036
1037        assert_eq!(unit_box.0.low, de_unit_box.0.low);
1038        assert_eq!(unit_box.0.scale, de_unit_box.0.scale);
1039    }
1040
1041    #[should_panic]
1042    #[test]
1043    fn test_uniform_bad_limits_equal_int() {
1044        Uniform::new(10, 10);
1045    }
1046
1047    #[test]
1048    fn test_uniform_good_limits_equal_int() {
1049        let mut rng = crate::test::rng(804);
1050        let dist = Uniform::new_inclusive(10, 10);
1051        for _ in 0..20 {
1052            assert_eq!(rng.sample(dist), 10);
1053        }
1054    }
1055
1056    #[should_panic]
1057    #[test]
1058    fn test_uniform_bad_limits_flipped_int() {
1059        Uniform::new(10, 5);
1060    }
1061
1062    #[test]
1063    #[cfg_attr(miri, ignore)] // Miri is too slow
1064    fn test_integers() {
1065        use core::{i128, u128};
1066        use core::{i16, i32, i64, i8, isize};
1067        use core::{u16, u32, u64, u8, usize};
1068
1069        let mut rng = crate::test::rng(251);
1070        macro_rules! t {
1071            ($ty:ident, $v:expr, $le:expr, $lt:expr) => {{
1072                for &(low, high) in $v.iter() {
1073                    let my_uniform = Uniform::new(low, high);
1074                    for _ in 0..1000 {
1075                        let v: $ty = rng.sample(my_uniform);
1076                        assert!($le(low, v) && $lt(v, high));
1077                    }
1078
1079                    let my_uniform = Uniform::new_inclusive(low, high);
1080                    for _ in 0..1000 {
1081                        let v: $ty = rng.sample(my_uniform);
1082                        assert!($le(low, v) && $le(v, high));
1083                    }
1084
1085                    let my_uniform = Uniform::new(&low, high);
1086                    for _ in 0..1000 {
1087                        let v: $ty = rng.sample(my_uniform);
1088                        assert!($le(low, v) && $lt(v, high));
1089                    }
1090
1091                    let my_uniform = Uniform::new_inclusive(&low, &high);
1092                    for _ in 0..1000 {
1093                        let v: $ty = rng.sample(my_uniform);
1094                        assert!($le(low, v) && $le(v, high));
1095                    }
1096
1097                    for _ in 0..1000 {
1098                        let v = <$ty as SampleUniform>::Sampler::sample_single(low, high, &mut rng);
1099                        assert!($le(low, v) && $lt(v, high));
1100                    }
1101
1102                    for _ in 0..1000 {
1103                        let v = <$ty as SampleUniform>::Sampler::sample_single_inclusive(low, high, &mut rng);
1104                        assert!($le(low, v) && $le(v, high));
1105                    }
1106                }
1107            }};
1108
1109            // scalar bulk
1110            ($($ty:ident),*) => {{
1111                $(t!(
1112                    $ty,
1113                    [(0, 10), (10, 127), ($ty::MIN, $ty::MAX)],
1114                    |x, y| x <= y,
1115                    |x, y| x < y
1116                );)*
1117            }};
1118
1119            // simd bulk
1120            ($($ty:ident),* => $scalar:ident) => {{
1121                $(t!(
1122                    $ty,
1123                    [
1124                        ($ty::splat(0), $ty::splat(10)),
1125                        ($ty::splat(10), $ty::splat(127)),
1126                        ($ty::splat($scalar::MIN), $ty::splat($scalar::MAX)),
1127                    ],
1128                    |x: $ty, y| x.le(y).all(),
1129                    |x: $ty, y| x.lt(y).all()
1130                );)*
1131            }};
1132        }
1133        t!(i8, i16, i32, i64, isize, u8, u16, u32, u64, usize, i128, u128);
1134    }
1135
1136    #[test]
1137    #[cfg_attr(miri, ignore)] // Miri is too slow
1138    fn test_char() {
1139        let mut rng = crate::test::rng(891);
1140        let mut max = core::char::from_u32(0).unwrap();
1141        for _ in 0..100 {
1142            let c = rng.gen_range('A'..='Z');
1143            assert!(('A'..='Z').contains(&c));
1144            max = max.max(c);
1145        }
1146        assert_eq!(max, 'Z');
1147        let d = Uniform::new(
1148            core::char::from_u32(0xD7F0).unwrap(),
1149            core::char::from_u32(0xE010).unwrap(),
1150        );
1151        for _ in 0..100 {
1152            let c = d.sample(&mut rng);
1153            assert!((c as u32) < 0xD800 || (c as u32) > 0xDFFF);
1154        }
1155    }
1156
1157    #[test]
1158    #[cfg_attr(miri, ignore)] // Miri is too slow
1159    fn test_floats() {
1160        let mut rng = crate::test::rng(252);
1161        let mut zero_rng = StepRng::new(0, 0);
1162        let mut max_rng = StepRng::new(0xffff_ffff_ffff_ffff, 0);
1163        macro_rules! t {
1164            ($ty:ty, $f_scalar:ident, $bits_shifted:expr) => {{
1165                let v: &[($f_scalar, $f_scalar)] = &[
1166                    (0.0, 100.0),
1167                    (-1e35, -1e25),
1168                    (1e-35, 1e-25),
1169                    (-1e35, 1e35),
1170                    (<$f_scalar>::from_bits(0), <$f_scalar>::from_bits(3)),
1171                    (-<$f_scalar>::from_bits(10), -<$f_scalar>::from_bits(1)),
1172                    (-<$f_scalar>::from_bits(5), 0.0),
1173                    (-<$f_scalar>::from_bits(7), -0.0),
1174                    (0.1 * ::core::$f_scalar::MAX, ::core::$f_scalar::MAX),
1175                    (-::core::$f_scalar::MAX * 0.2, ::core::$f_scalar::MAX * 0.7),
1176                ];
1177                for &(low_scalar, high_scalar) in v.iter() {
1178                    for lane in 0..<$ty>::lanes() {
1179                        let low = <$ty>::splat(0.0 as $f_scalar).replace(lane, low_scalar);
1180                        let high = <$ty>::splat(1.0 as $f_scalar).replace(lane, high_scalar);
1181                        let my_uniform = Uniform::new(low, high);
1182                        let my_incl_uniform = Uniform::new_inclusive(low, high);
1183                        for _ in 0..100 {
1184                            let v = rng.sample(my_uniform).extract(lane);
1185                            assert!(low_scalar <= v && v < high_scalar);
1186                            let v = rng.sample(my_incl_uniform).extract(lane);
1187                            assert!(low_scalar <= v && v <= high_scalar);
1188                            let v = <$ty as SampleUniform>::Sampler
1189                                ::sample_single(low, high, &mut rng).extract(lane);
1190                            assert!(low_scalar <= v && v < high_scalar);
1191                        }
1192
1193                        assert_eq!(
1194                            rng.sample(Uniform::new_inclusive(low, low)).extract(lane),
1195                            low_scalar
1196                        );
1197
1198                        assert_eq!(zero_rng.sample(my_uniform).extract(lane), low_scalar);
1199                        assert_eq!(zero_rng.sample(my_incl_uniform).extract(lane), low_scalar);
1200                        assert_eq!(<$ty as SampleUniform>::Sampler
1201                            ::sample_single(low, high, &mut zero_rng)
1202                            .extract(lane), low_scalar);
1203                        assert!(max_rng.sample(my_uniform).extract(lane) < high_scalar);
1204                        assert!(max_rng.sample(my_incl_uniform).extract(lane) <= high_scalar);
1205
1206                        // Don't run this test for really tiny differences between high and low
1207                        // since for those rounding might result in selecting high for a very
1208                        // long time.
1209                        if (high_scalar - low_scalar) > 0.0001 {
1210                            let mut lowering_max_rng = StepRng::new(
1211                                0xffff_ffff_ffff_ffff,
1212                                (-1i64 << $bits_shifted) as u64,
1213                            );
1214                            assert!(
1215                                <$ty as SampleUniform>::Sampler
1216                                    ::sample_single(low, high, &mut lowering_max_rng)
1217                                    .extract(lane) < high_scalar
1218                            );
1219                        }
1220                    }
1221                }
1222
1223                assert_eq!(
1224                    rng.sample(Uniform::new_inclusive(
1225                        ::core::$f_scalar::MAX,
1226                        ::core::$f_scalar::MAX
1227                    )),
1228                    ::core::$f_scalar::MAX
1229                );
1230                assert_eq!(
1231                    rng.sample(Uniform::new_inclusive(
1232                        -::core::$f_scalar::MAX,
1233                        -::core::$f_scalar::MAX
1234                    )),
1235                    -::core::$f_scalar::MAX
1236                );
1237            }};
1238        }
1239
1240        t!(f32, f32, 32 - 23);
1241        t!(f64, f64, 64 - 52);
1242    }
1243
1244    #[test]
1245    #[should_panic]
1246    fn test_float_overflow() {
1247        let _ = Uniform::from(::core::f64::MIN..::core::f64::MAX);
1248    }
1249
1250    #[test]
1251    #[should_panic]
1252    fn test_float_overflow_single() {
1253        let mut rng = crate::test::rng(252);
1254        rng.gen_range(::core::f64::MIN..::core::f64::MAX);
1255    }
1256
1257    #[test]
1258    #[cfg(all(
1259        feature = "std",
1260        not(target_arch = "wasm32"),
1261    ))]
1262    fn test_float_assertions() {
1263        use super::SampleUniform;
1264        use std::panic::catch_unwind;
1265        fn range<T: SampleUniform>(low: T, high: T) {
1266            let mut rng = crate::test::rng(253);
1267            T::Sampler::sample_single(low, high, &mut rng);
1268        }
1269
1270        macro_rules! t {
1271            ($ty:ident, $f_scalar:ident) => {{
1272                let v: &[($f_scalar, $f_scalar)] = &[
1273                    (::std::$f_scalar::NAN, 0.0),
1274                    (1.0, ::std::$f_scalar::NAN),
1275                    (::std::$f_scalar::NAN, ::std::$f_scalar::NAN),
1276                    (1.0, 0.5),
1277                    (::std::$f_scalar::MAX, -::std::$f_scalar::MAX),
1278                    (::std::$f_scalar::INFINITY, ::std::$f_scalar::INFINITY),
1279                    (
1280                        ::std::$f_scalar::NEG_INFINITY,
1281                        ::std::$f_scalar::NEG_INFINITY,
1282                    ),
1283                    (::std::$f_scalar::NEG_INFINITY, 5.0),
1284                    (5.0, ::std::$f_scalar::INFINITY),
1285                    (::std::$f_scalar::NAN, ::std::$f_scalar::INFINITY),
1286                    (::std::$f_scalar::NEG_INFINITY, ::std::$f_scalar::NAN),
1287                    (::std::$f_scalar::NEG_INFINITY, ::std::$f_scalar::INFINITY),
1288                ];
1289                for &(low_scalar, high_scalar) in v.iter() {
1290                    for lane in 0..<$ty>::lanes() {
1291                        let low = <$ty>::splat(0.0 as $f_scalar).replace(lane, low_scalar);
1292                        let high = <$ty>::splat(1.0 as $f_scalar).replace(lane, high_scalar);
1293                        assert!(catch_unwind(|| range(low, high)).is_err());
1294                        assert!(catch_unwind(|| Uniform::new(low, high)).is_err());
1295                        assert!(catch_unwind(|| Uniform::new_inclusive(low, high)).is_err());
1296                        assert!(catch_unwind(|| range(low, low)).is_err());
1297                        assert!(catch_unwind(|| Uniform::new(low, low)).is_err());
1298                    }
1299                }
1300            }};
1301        }
1302
1303        t!(f32, f32);
1304        t!(f64, f64);
1305    }
1306
1307
1308    #[test]
1309    #[cfg_attr(miri, ignore)] // Miri is too slow
1310    fn test_durations() {
1311        let mut rng = crate::test::rng(253);
1312
1313        let v = &[
1314            (Duration::new(10, 50000), Duration::new(100, 1234)),
1315            (Duration::new(0, 100), Duration::new(1, 50)),
1316            (
1317                Duration::new(0, 0),
1318                Duration::new(u64::max_value(), 999_999_999),
1319            ),
1320        ];
1321        for &(low, high) in v.iter() {
1322            let my_uniform = Uniform::new(low, high);
1323            for _ in 0..1000 {
1324                let v = rng.sample(my_uniform);
1325                assert!(low <= v && v < high);
1326            }
1327        }
1328    }
1329
1330    #[test]
1331    fn test_custom_uniform() {
1332        use crate::distributions::uniform::{
1333            SampleBorrow, SampleUniform, UniformFloat, UniformSampler,
1334        };
1335        #[derive(Clone, Copy, PartialEq, PartialOrd)]
1336        struct MyF32 {
1337            x: f32,
1338        }
1339        #[derive(Clone, Copy, Debug)]
1340        struct UniformMyF32(UniformFloat<f32>);
1341        impl UniformSampler for UniformMyF32 {
1342            type X = MyF32;
1343
1344            fn new<B1, B2>(low: B1, high: B2) -> Self
1345            where
1346                B1: SampleBorrow<Self::X> + Sized,
1347                B2: SampleBorrow<Self::X> + Sized,
1348            {
1349                UniformMyF32(UniformFloat::<f32>::new(low.borrow().x, high.borrow().x))
1350            }
1351
1352            fn new_inclusive<B1, B2>(low: B1, high: B2) -> Self
1353            where
1354                B1: SampleBorrow<Self::X> + Sized,
1355                B2: SampleBorrow<Self::X> + Sized,
1356            {
1357                UniformSampler::new(low, high)
1358            }
1359
1360            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
1361                MyF32 {
1362                    x: self.0.sample(rng),
1363                }
1364            }
1365        }
1366        impl SampleUniform for MyF32 {
1367            type Sampler = UniformMyF32;
1368        }
1369
1370        let (low, high) = (MyF32 { x: 17.0f32 }, MyF32 { x: 22.0f32 });
1371        let uniform = Uniform::new(low, high);
1372        let mut rng = crate::test::rng(804);
1373        for _ in 0..100 {
1374            let x: MyF32 = rng.sample(uniform);
1375            assert!(low <= x && x < high);
1376        }
1377    }
1378
1379    #[test]
1380    fn test_uniform_from_std_range() {
1381        let r = Uniform::from(2u32..7);
1382        assert_eq!(r.0.low, 2);
1383        assert_eq!(r.0.range, 5);
1384        let r = Uniform::from(2.0f64..7.0);
1385        assert_eq!(r.0.low, 2.0);
1386        assert_eq!(r.0.scale, 5.0);
1387    }
1388
1389    #[test]
1390    fn test_uniform_from_std_range_inclusive() {
1391        let r = Uniform::from(2u32..=6);
1392        assert_eq!(r.0.low, 2);
1393        assert_eq!(r.0.range, 5);
1394        let r = Uniform::from(2.0f64..=7.0);
1395        assert_eq!(r.0.low, 2.0);
1396        assert!(r.0.scale > 5.0);
1397        assert!(r.0.scale < 5.0 + 1e-14);
1398    }
1399
1400    #[test]
1401    fn value_stability() {
1402        fn test_samples<T: SampleUniform + Copy + core::fmt::Debug + PartialEq>(
1403            lb: T, ub: T, expected_single: &[T], expected_multiple: &[T],
1404        ) where Uniform<T>: Distribution<T> {
1405            let mut rng = crate::test::rng(897);
1406            let mut buf = [lb; 3];
1407
1408            for x in &mut buf {
1409                *x = T::Sampler::sample_single(lb, ub, &mut rng);
1410            }
1411            assert_eq!(&buf, expected_single);
1412
1413            let distr = Uniform::new(lb, ub);
1414            for x in &mut buf {
1415                *x = rng.sample(&distr);
1416            }
1417            assert_eq!(&buf, expected_multiple);
1418        }
1419
1420        // We test on a sub-set of types; possibly we should do more.
1421        // TODO: SIMD types
1422
1423        test_samples(11u8, 219, &[17, 66, 214], &[181, 93, 165]);
1424        test_samples(11u32, 219, &[17, 66, 214], &[181, 93, 165]);
1425
1426        test_samples(0f32, 1e-2f32, &[0.0003070104, 0.0026630748, 0.00979833], &[
1427            0.008194133,
1428            0.00398172,
1429            0.007428536,
1430        ]);
1431        test_samples(
1432            -1e10f64,
1433            1e10f64,
1434            &[-4673848682.871551, 6388267422.932352, 4857075081.198343],
1435            &[1173375212.1808167, 1917642852.109581, 2365076174.3153973],
1436        );
1437
1438        test_samples(
1439            Duration::new(2, 0),
1440            Duration::new(4, 0),
1441            &[
1442                Duration::new(2, 532615131),
1443                Duration::new(3, 638826742),
1444                Duration::new(3, 485707508),
1445            ],
1446            &[
1447                Duration::new(3, 117337521),
1448                Duration::new(3, 191764285),
1449                Duration::new(3, 236507617),
1450            ],
1451        );
1452    }
1453
1454    #[test]
1455    fn uniform_distributions_can_be_compared() {
1456        assert_eq!(Uniform::new(1.0, 2.0), Uniform::new(1.0, 2.0));
1457
1458        // To cover UniformInt
1459        assert_eq!(Uniform::new(1 as u32, 2 as u32), Uniform::new(1 as u32, 2 as u32));
1460    }
1461}
rand/distributions/uniform.rs

rand/distributions/
uniform.rs
