logo
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
// Copyright 2018 Developers of the Rand project.
// Copyright 2013-2017 The Rust Project Developers.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! [`Rng`] trait

use rand_core::{Error, RngCore};
use crate::distributions::uniform::{SampleRange, SampleUniform};
use crate::distributions::{self, Distribution, Standard};
use core::num::Wrapping;
use core::{mem, slice};

/// An automatically-implemented extension trait on [`RngCore`] providing high-level
/// generic methods for sampling values and other convenience methods.
///
/// This is the primary trait to use when generating random values.
///
/// # Generic usage
///
/// The basic pattern is `fn foo<R: Rng + ?Sized>(rng: &mut R)`. Some
/// things are worth noting here:
///
/// - Since `Rng: RngCore` and every `RngCore` implements `Rng`, it makes no
///   difference whether we use `R: Rng` or `R: RngCore`.
/// - The `+ ?Sized` un-bounding allows functions to be called directly on
///   type-erased references; i.e. `foo(r)` where `r: &mut dyn RngCore`. Without
///   this it would be necessary to write `foo(&mut r)`.
///
/// An alternative pattern is possible: `fn foo<R: Rng>(rng: R)`. This has some
/// trade-offs. It allows the argument to be consumed directly without a `&mut`
/// (which is how `from_rng(thread_rng())` works); also it still works directly
/// on references (including type-erased references). Unfortunately within the
/// function `foo` it is not known whether `rng` is a reference type or not,
/// hence many uses of `rng` require an extra reference, either explicitly
/// (`distr.sample(&mut rng)`) or implicitly (`rng.gen()`); one may hope the
/// optimiser can remove redundant references later.
///
/// Example:
///
/// ```
/// # use rand::thread_rng;
/// use rand::Rng;
///
/// fn foo<R: Rng + ?Sized>(rng: &mut R) -> f32 {
///     rng.gen()
/// }
///
/// # let v = foo(&mut thread_rng());
/// ```
pub trait Rng: RngCore {
    /// Return a random value supporting the [`Standard`] distribution.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut rng = thread_rng();
    /// let x: u32 = rng.gen();
    /// println!("{}", x);
    /// println!("{:?}", rng.gen::<(f64, bool)>());
    /// ```
    ///
    /// # Arrays and tuples
    ///
    /// The `rng.gen()` method is able to generate arrays
    /// and tuples (up to 12 elements), so long as all element types can be
    /// generated.
    ///
    /// For arrays of integers, especially for those with small element types
    /// (< 64 bit), it will likely be faster to instead use [`Rng::fill`].
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut rng = thread_rng();
    /// let tuple: (u8, i32, char) = rng.gen(); // arbitrary tuple support
    ///
    /// let arr1: [f32; 32] = rng.gen();        // array construction
    /// let mut arr2 = [0u8; 128];
    /// rng.fill(&mut arr2);                    // array fill
    /// ```
    ///
    /// [`Standard`]: distributions::Standard
    #[inline]
    fn gen<T>(&mut self) -> T
    where Standard: Distribution<T> {
        Standard.sample(self)
    }

    /// Generate a random value in the given range.
    ///
    /// This function is optimised for the case that only a single sample is
    /// made from the given range. See also the [`Uniform`] distribution
    /// type which may be faster if sampling from the same range repeatedly.
    ///
    /// Only `gen_range(low..high)` and `gen_range(low..=high)` are supported.
    ///
    /// # Panics
    ///
    /// Panics if the range is empty.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut rng = thread_rng();
    ///
    /// // Exclusive range
    /// let n: u32 = rng.gen_range(0..10);
    /// println!("{}", n);
    /// let m: f64 = rng.gen_range(-40.0..1.3e5);
    /// println!("{}", m);
    ///
    /// // Inclusive range
    /// let n: u32 = rng.gen_range(0..=10);
    /// println!("{}", n);
    /// ```
    ///
    /// [`Uniform`]: distributions::uniform::Uniform
    fn gen_range<T, R>(&mut self, range: R) -> T
    where
        T: SampleUniform,
        R: SampleRange<T>
    {
        assert!(!range.is_empty(), "cannot sample empty range");
        range.sample_single(self)
    }

    /// Sample a new value, using the given distribution.
    ///
    /// ### Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    /// use rand::distributions::Uniform;
    ///
    /// let mut rng = thread_rng();
    /// let x = rng.sample(Uniform::new(10u32, 15));
    /// // Type annotation requires two types, the type and distribution; the
    /// // distribution can be inferred.
    /// let y = rng.sample::<u16, _>(Uniform::new(10, 15));
    /// ```
    fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T {
        distr.sample(self)
    }

    /// Create an iterator that generates values using the given distribution.
    ///
    /// Note that this function takes its arguments by value. This works since
    /// `(&mut R): Rng where R: Rng` and
    /// `(&D): Distribution where D: Distribution`,
    /// however borrowing is not automatic hence `rng.sample_iter(...)` may
    /// need to be replaced with `(&mut rng).sample_iter(...)`.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    /// use rand::distributions::{Alphanumeric, Uniform, Standard};
    ///
    /// let mut rng = thread_rng();
    ///
    /// // Vec of 16 x f32:
    /// let v: Vec<f32> = (&mut rng).sample_iter(Standard).take(16).collect();
    ///
    /// // String:
    /// let s: String = (&mut rng).sample_iter(Alphanumeric)
    ///     .take(7)
    ///     .map(char::from)
    ///     .collect();
    ///
    /// // Combined values
    /// println!("{:?}", (&mut rng).sample_iter(Standard).take(5)
    ///                              .collect::<Vec<(f64, bool)>>());
    ///
    /// // Dice-rolling:
    /// let die_range = Uniform::new_inclusive(1, 6);
    /// let mut roll_die = (&mut rng).sample_iter(die_range);
    /// while roll_die.next().unwrap() != 6 {
    ///     println!("Not a 6; rolling again!");
    /// }
    /// ```
    fn sample_iter<T, D>(self, distr: D) -> distributions::DistIter<D, Self, T>
    where
        D: Distribution<T>,
        Self: Sized,
    {
        distr.sample_iter(self)
    }

    /// Fill any type implementing [`Fill`] with random data
    ///
    /// The distribution is expected to be uniform with portable results, but
    /// this cannot be guaranteed for third-party implementations.
    ///
    /// This is identical to [`try_fill`] except that it panics on error.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut arr = [0i8; 20];
    /// thread_rng().fill(&mut arr[..]);
    /// ```
    ///
    /// [`fill_bytes`]: RngCore::fill_bytes
    /// [`try_fill`]: Rng::try_fill
    fn fill<T: Fill + ?Sized>(&mut self, dest: &mut T) {
        dest.try_fill(self).unwrap_or_else(|_| panic!("Rng::fill failed"))
    }

    /// Fill any type implementing [`Fill`] with random data
    ///
    /// The distribution is expected to be uniform with portable results, but
    /// this cannot be guaranteed for third-party implementations.
    ///
    /// This is identical to [`fill`] except that it forwards errors.
    ///
    /// # Example
    ///
    /// ```
    /// # use rand::Error;
    /// use rand::{thread_rng, Rng};
    ///
    /// # fn try_inner() -> Result<(), Error> {
    /// let mut arr = [0u64; 4];
    /// thread_rng().try_fill(&mut arr[..])?;
    /// # Ok(())
    /// # }
    ///
    /// # try_inner().unwrap()
    /// ```
    ///
    /// [`try_fill_bytes`]: RngCore::try_fill_bytes
    /// [`fill`]: Rng::fill
    fn try_fill<T: Fill + ?Sized>(&mut self, dest: &mut T) -> Result<(), Error> {
        dest.try_fill(self)
    }

    /// Return a bool with a probability `p` of being true.
    ///
    /// See also the [`Bernoulli`] distribution, which may be faster if
    /// sampling from the same probability repeatedly.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut rng = thread_rng();
    /// println!("{}", rng.gen_bool(1.0 / 3.0));
    /// ```
    ///
    /// # Panics
    ///
    /// If `p < 0` or `p > 1`.
    ///
    /// [`Bernoulli`]: distributions::Bernoulli
    #[inline]
    fn gen_bool(&mut self, p: f64) -> bool {
        let d = distributions::Bernoulli::new(p).unwrap();
        self.sample(d)
    }

    /// Return a bool with a probability of `numerator/denominator` of being
    /// true. I.e. `gen_ratio(2, 3)` has chance of 2 in 3, or about 67%, of
    /// returning true. If `numerator == denominator`, then the returned value
    /// is guaranteed to be `true`. If `numerator == 0`, then the returned
    /// value is guaranteed to be `false`.
    ///
    /// See also the [`Bernoulli`] distribution, which may be faster if
    /// sampling from the same `numerator` and `denominator` repeatedly.
    ///
    /// # Panics
    ///
    /// If `denominator == 0` or `numerator > denominator`.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut rng = thread_rng();
    /// println!("{}", rng.gen_ratio(2, 3));
    /// ```
    ///
    /// [`Bernoulli`]: distributions::Bernoulli
    #[inline]
    fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool {
        let d = distributions::Bernoulli::from_ratio(numerator, denominator).unwrap();
        self.sample(d)
    }
}

impl<R: RngCore + ?Sized> Rng for R {}

/// Types which may be filled with random data
///
/// This trait allows arrays to be efficiently filled with random data.
///
/// Implementations are expected to be portable across machines unless
/// clearly documented otherwise (see the
/// [Chapter on Portability](https://rust-random.github.io/book/portability.html)).
pub trait Fill {
    /// Fill self with random data
    fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error>;
}

macro_rules! impl_fill_each {
    () => {};
    ($t:ty) => {
        impl Fill for [$t] {
            fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
                for elt in self.iter_mut() {
                    *elt = rng.gen();
                }
                Ok(())
            }
        }
    };
    ($t:ty, $($tt:ty,)*) => {
        impl_fill_each!($t);
        impl_fill_each!($($tt,)*);
    };
}

impl_fill_each!(bool, char, f32, f64,);

impl Fill for [u8] {
    fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
        rng.try_fill_bytes(self)
    }
}

macro_rules! impl_fill {
    () => {};
    ($t:ty) => {
        impl Fill for [$t] {
            #[inline(never)] // in micro benchmarks, this improves performance
            fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
                if self.len() > 0 {
                    rng.try_fill_bytes(unsafe {
                        slice::from_raw_parts_mut(self.as_mut_ptr()
                            as *mut u8,
                            self.len() * mem::size_of::<$t>()
                        )
                    })?;
                    for x in self {
                        *x = x.to_le();
                    }
                }
                Ok(())
            }
        }

        impl Fill for [Wrapping<$t>] {
            #[inline(never)]
            fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
                if self.len() > 0 {
                    rng.try_fill_bytes(unsafe {
                        slice::from_raw_parts_mut(self.as_mut_ptr()
                            as *mut u8,
                            self.len() * mem::size_of::<$t>()
                        )
                    })?;
                    for x in self {
                    *x = Wrapping(x.0.to_le());
                    }
                }
                Ok(())
            }
        }
    };
    ($t:ty, $($tt:ty,)*) => {
        impl_fill!($t);
        // TODO: this could replace above impl once Rust #32463 is fixed
        // impl_fill!(Wrapping<$t>);
        impl_fill!($($tt,)*);
    }
}

impl_fill!(u16, u32, u64, usize, u128,);
impl_fill!(i8, i16, i32, i64, isize, i128,);

impl<T, const N: usize> Fill for [T; N]
where [T]: Fill
{
    fn try_fill<R: Rng + ?Sized>(&mut self, rng: &mut R) -> Result<(), Error> {
        self[..].try_fill(rng)
    }
}

#[cfg(test)]
mod test {
    use super::*;
    use crate::test::rng;
    use crate::rngs::mock::StepRng;
    #[cfg(feature = "alloc")] use alloc::boxed::Box;

    #[test]
    fn test_fill_bytes_default() {
        let mut r = StepRng::new(0x11_22_33_44_55_66_77_88, 0);

        // check every remainder mod 8, both in small and big vectors.
        let lengths = [0, 1, 2, 3, 4, 5, 6, 7, 80, 81, 82, 83, 84, 85, 86, 87];
        for &n in lengths.iter() {
            let mut buffer = [0u8; 87];
            let v = &mut buffer[0..n];
            r.fill_bytes(v);

            // use this to get nicer error messages.
            for (i, &byte) in v.iter().enumerate() {
                if byte == 0 {
                    panic!("byte {} of {} is zero", i, n)
                }
            }
        }
    }

    #[test]
    fn test_fill() {
        let x = 9041086907909331047; // a random u64
        let mut rng = StepRng::new(x, 0);

        // Convert to byte sequence and back to u64; byte-swap twice if BE.
        let mut array = [0u64; 2];
        rng.fill(&mut array[..]);
        assert_eq!(array, [x, x]);
        assert_eq!(rng.next_u64(), x);

        // Convert to bytes then u32 in LE order
        let mut array = [0u32; 2];
        rng.fill(&mut array[..]);
        assert_eq!(array, [x as u32, (x >> 32) as u32]);
        assert_eq!(rng.next_u32(), x as u32);

        // Check equivalence using wrapped arrays
        let mut warray = [Wrapping(0u32); 2];
        rng.fill(&mut warray[..]);
        assert_eq!(array[0], warray[0].0);
        assert_eq!(array[1], warray[1].0);

        // Check equivalence for generated floats
        let mut array = [0f32; 2];
        rng.fill(&mut array);
        let gen: [f32; 2] = rng.gen();
        assert_eq!(array, gen);
    }

    #[test]
    fn test_fill_empty() {
        let mut array = [0u32; 0];
        let mut rng = StepRng::new(0, 1);
        rng.fill(&mut array);
        rng.fill(&mut array[..]);
    }

    #[test]
    fn test_gen_range_int() {
        let mut r = rng(101);
        for _ in 0..1000 {
            let a = r.gen_range(-4711..17);
            assert!((-4711..17).contains(&a));
            let a: i8 = r.gen_range(-3..42);
            assert!((-3..42).contains(&a));
            let a: u16 = r.gen_range(10..99);
            assert!((10..99).contains(&a));
            let a: i32 = r.gen_range(-100..2000);
            assert!((-100..2000).contains(&a));
            let a: u32 = r.gen_range(12..=24);
            assert!((12..=24).contains(&a));

            assert_eq!(r.gen_range(0u32..1), 0u32);
            assert_eq!(r.gen_range(-12i64..-11), -12i64);
            assert_eq!(r.gen_range(3_000_000..3_000_001), 3_000_000);
        }
    }

    #[test]
    fn test_gen_range_float() {
        let mut r = rng(101);
        for _ in 0..1000 {
            let a = r.gen_range(-4.5..1.7);
            assert!((-4.5..1.7).contains(&a));
            let a = r.gen_range(-1.1..=-0.3);
            assert!((-1.1..=-0.3).contains(&a));

            assert_eq!(r.gen_range(0.0f32..=0.0), 0.);
            assert_eq!(r.gen_range(-11.0..=-11.0), -11.);
            assert_eq!(r.gen_range(3_000_000.0..=3_000_000.0), 3_000_000.);
        }
    }

    #[test]
    #[should_panic]
    fn test_gen_range_panic_int() {
        #![allow(clippy::reversed_empty_ranges)]
        let mut r = rng(102);
        r.gen_range(5..-2);
    }

    #[test]
    #[should_panic]
    fn test_gen_range_panic_usize() {
        #![allow(clippy::reversed_empty_ranges)]
        let mut r = rng(103);
        r.gen_range(5..2);
    }

    #[test]
    fn test_gen_bool() {
        #![allow(clippy::bool_assert_comparison)]

        let mut r = rng(105);
        for _ in 0..5 {
            assert_eq!(r.gen_bool(0.0), false);
            assert_eq!(r.gen_bool(1.0), true);
        }
    }

    #[test]
    fn test_rng_trait_object() {
        use crate::distributions::{Distribution, Standard};
        let mut rng = rng(109);
        let mut r = &mut rng as &mut dyn RngCore;
        r.next_u32();
        r.gen::<i32>();
        assert_eq!(r.gen_range(0..1), 0);
        let _c: u8 = Standard.sample(&mut r);
    }

    #[test]
    #[cfg(feature = "alloc")]
    fn test_rng_boxed_trait() {
        use crate::distributions::{Distribution, Standard};
        let rng = rng(110);
        let mut r = Box::new(rng) as Box<dyn RngCore>;
        r.next_u32();
        r.gen::<i32>();
        assert_eq!(r.gen_range(0..1), 0);
        let _c: u8 = Standard.sample(&mut r);
    }

    #[test]
    #[cfg_attr(miri, ignore)] // Miri is too slow
    fn test_gen_ratio_average() {
        const NUM: u32 = 3;
        const DENOM: u32 = 10;
        const N: u32 = 100_000;

        let mut sum: u32 = 0;
        let mut rng = rng(111);
        for _ in 0..N {
            if rng.gen_ratio(NUM, DENOM) {
                sum += 1;
            }
        }
        // Have Binomial(N, NUM/DENOM) distribution
        let expected = (NUM * N) / DENOM; // exact integer
        assert!(((sum - expected) as i32).abs() < 500);
    }
}