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| 1 | +// Copyright 2018 Developers of the Rand project. |
| 2 | +// Copyright 2013-2017 The Rust Project Developers. |
| 3 | +// |
| 4 | +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or |
| 5 | +// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license |
| 6 | +// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your |
| 7 | +// option. This file may not be copied, modified, or distributed |
| 8 | +// except according to those terms. |
| 9 | + |
| 10 | +//! Distribution trait and associates |
| 11 | +
|
| 12 | +use crate::Rng; |
| 13 | +use core::iter; |
| 14 | +#[cfg(feature = "alloc")] |
| 15 | +use alloc::string::String; |
| 16 | + |
| 17 | +/// Types (distributions) that can be used to create a random instance of `T`. |
| 18 | +/// |
| 19 | +/// It is possible to sample from a distribution through both the |
| 20 | +/// `Distribution` and [`Rng`] traits, via `distr.sample(&mut rng)` and |
| 21 | +/// `rng.sample(distr)`. They also both offer the [`sample_iter`] method, which |
| 22 | +/// produces an iterator that samples from the distribution. |
| 23 | +/// |
| 24 | +/// All implementations are expected to be immutable; this has the significant |
| 25 | +/// advantage of not needing to consider thread safety, and for most |
| 26 | +/// distributions efficient state-less sampling algorithms are available. |
| 27 | +/// |
| 28 | +/// Implementations are typically expected to be portable with reproducible |
| 29 | +/// results when used with a PRNG with fixed seed; see the |
| 30 | +/// [portability chapter](https://rust-random.github.io/book/portability.html) |
| 31 | +/// of The Rust Rand Book. In some cases this does not apply, e.g. the `usize` |
| 32 | +/// type requires different sampling on 32-bit and 64-bit machines. |
| 33 | +/// |
| 34 | +/// [`sample_iter`]: Distribution::sample_iter |
| 35 | +pub trait Distribution<T> { |
| 36 | + /// Generate a random value of `T`, using `rng` as the source of randomness. |
| 37 | + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T; |
| 38 | + |
| 39 | + /// Create an iterator that generates random values of `T`, using `rng` as |
| 40 | + /// the source of randomness. |
| 41 | + /// |
| 42 | + /// Note that this function takes `self` by value. This works since |
| 43 | + /// `Distribution<T>` is impl'd for `&D` where `D: Distribution<T>`, |
| 44 | + /// however borrowing is not automatic hence `distr.sample_iter(...)` may |
| 45 | + /// need to be replaced with `(&distr).sample_iter(...)` to borrow or |
| 46 | + /// `(&*distr).sample_iter(...)` to reborrow an existing reference. |
| 47 | + /// |
| 48 | + /// # Example |
| 49 | + /// |
| 50 | + /// ``` |
| 51 | + /// use rand::thread_rng; |
| 52 | + /// use rand::distributions::{Distribution, Alphanumeric, Uniform, Standard}; |
| 53 | + /// |
| 54 | + /// let mut rng = thread_rng(); |
| 55 | + /// |
| 56 | + /// // Vec of 16 x f32: |
| 57 | + /// let v: Vec<f32> = Standard.sample_iter(&mut rng).take(16).collect(); |
| 58 | + /// |
| 59 | + /// // String: |
| 60 | + /// let s: String = Alphanumeric |
| 61 | + /// .sample_iter(&mut rng) |
| 62 | + /// .take(7) |
| 63 | + /// .map(char::from) |
| 64 | + /// .collect(); |
| 65 | + /// |
| 66 | + /// // Dice-rolling: |
| 67 | + /// let die_range = Uniform::new_inclusive(1, 6); |
| 68 | + /// let mut roll_die = die_range.sample_iter(&mut rng); |
| 69 | + /// while roll_die.next().unwrap() != 6 { |
| 70 | + /// println!("Not a 6; rolling again!"); |
| 71 | + /// } |
| 72 | + /// ``` |
| 73 | + fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> |
| 74 | + where |
| 75 | + R: Rng, |
| 76 | + Self: Sized, |
| 77 | + { |
| 78 | + DistIter { |
| 79 | + distr: self, |
| 80 | + rng, |
| 81 | + phantom: ::core::marker::PhantomData, |
| 82 | + } |
| 83 | + } |
| 84 | + |
| 85 | + /// Create a distribution of values of 'S' by mapping the output of `Self` |
| 86 | + /// through the closure `F` |
| 87 | + /// |
| 88 | + /// # Example |
| 89 | + /// |
| 90 | + /// ``` |
| 91 | + /// use rand::thread_rng; |
| 92 | + /// use rand::distributions::{Distribution, Uniform}; |
| 93 | + /// |
| 94 | + /// let mut rng = thread_rng(); |
| 95 | + /// |
| 96 | + /// let die = Uniform::new_inclusive(1, 6); |
| 97 | + /// let even_number = die.map(|num| num % 2 == 0); |
| 98 | + /// while !even_number.sample(&mut rng) { |
| 99 | + /// println!("Still odd; rolling again!"); |
| 100 | + /// } |
| 101 | + /// ``` |
| 102 | + fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S> |
| 103 | + where |
| 104 | + F: Fn(T) -> S, |
| 105 | + Self: Sized, |
| 106 | + { |
| 107 | + DistMap { |
| 108 | + distr: self, |
| 109 | + func, |
| 110 | + phantom: ::core::marker::PhantomData, |
| 111 | + } |
| 112 | + } |
| 113 | +} |
| 114 | + |
| 115 | +impl<'a, T, D: Distribution<T>> Distribution<T> for &'a D { |
| 116 | + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T { |
| 117 | + (*self).sample(rng) |
| 118 | + } |
| 119 | +} |
| 120 | + |
| 121 | +/// An iterator that generates random values of `T` with distribution `D`, |
| 122 | +/// using `R` as the source of randomness. |
| 123 | +/// |
| 124 | +/// This `struct` is created by the [`sample_iter`] method on [`Distribution`]. |
| 125 | +/// See its documentation for more. |
| 126 | +/// |
| 127 | +/// [`sample_iter`]: Distribution::sample_iter |
| 128 | +#[derive(Debug)] |
| 129 | +pub struct DistIter<D, R, T> { |
| 130 | + distr: D, |
| 131 | + rng: R, |
| 132 | + phantom: ::core::marker::PhantomData<T>, |
| 133 | +} |
| 134 | + |
| 135 | +impl<D, R, T> Iterator for DistIter<D, R, T> |
| 136 | +where |
| 137 | + D: Distribution<T>, |
| 138 | + R: Rng, |
| 139 | +{ |
| 140 | + type Item = T; |
| 141 | + |
| 142 | + #[inline(always)] |
| 143 | + fn next(&mut self) -> Option<T> { |
| 144 | + // Here, self.rng may be a reference, but we must take &mut anyway. |
| 145 | + // Even if sample could take an R: Rng by value, we would need to do this |
| 146 | + // since Rng is not copyable and we cannot enforce that this is "reborrowable". |
| 147 | + Some(self.distr.sample(&mut self.rng)) |
| 148 | + } |
| 149 | + |
| 150 | + fn size_hint(&self) -> (usize, Option<usize>) { |
| 151 | + (usize::max_value(), None) |
| 152 | + } |
| 153 | +} |
| 154 | + |
| 155 | +impl<D, R, T> iter::FusedIterator for DistIter<D, R, T> |
| 156 | +where |
| 157 | + D: Distribution<T>, |
| 158 | + R: Rng, |
| 159 | +{ |
| 160 | +} |
| 161 | + |
| 162 | +#[cfg(features = "nightly")] |
| 163 | +impl<D, R, T> iter::TrustedLen for DistIter<D, R, T> |
| 164 | +where |
| 165 | + D: Distribution<T>, |
| 166 | + R: Rng, |
| 167 | +{ |
| 168 | +} |
| 169 | + |
| 170 | +/// A distribution of values of type `S` derived from the distribution `D` |
| 171 | +/// by mapping its output of type `T` through the closure `F`. |
| 172 | +/// |
| 173 | +/// This `struct` is created by the [`Distribution::map`] method. |
| 174 | +/// See its documentation for more. |
| 175 | +#[derive(Debug)] |
| 176 | +pub struct DistMap<D, F, T, S> { |
| 177 | + distr: D, |
| 178 | + func: F, |
| 179 | + phantom: ::core::marker::PhantomData<fn(T) -> S>, |
| 180 | +} |
| 181 | + |
| 182 | +impl<D, F, T, S> Distribution<S> for DistMap<D, F, T, S> |
| 183 | +where |
| 184 | + D: Distribution<T>, |
| 185 | + F: Fn(T) -> S, |
| 186 | +{ |
| 187 | + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> S { |
| 188 | + (self.func)(self.distr.sample(rng)) |
| 189 | + } |
| 190 | +} |
| 191 | + |
| 192 | +/// `String` sampler |
| 193 | +/// |
| 194 | +/// Sampling a `String` of random characters is not quite the same as collecting |
| 195 | +/// a sequence of chars. This trait contains some helpers. |
| 196 | +#[cfg(feature = "alloc")] |
| 197 | +pub trait DistString { |
| 198 | + /// Append `len` random chars to `string` |
| 199 | + fn append_string<R: Rng + ?Sized>(&self, rng: &mut R, string: &mut String, len: usize); |
| 200 | + |
| 201 | + /// Generate a `String` of `len` random chars |
| 202 | + #[inline] |
| 203 | + fn sample_string<R: Rng + ?Sized>(&self, rng: &mut R, len: usize) -> String { |
| 204 | + let mut s = String::new(); |
| 205 | + self.append_string(rng, &mut s, len); |
| 206 | + s |
| 207 | + } |
| 208 | +} |
| 209 | + |
| 210 | +#[cfg(test)] |
| 211 | +mod tests { |
| 212 | + use crate::distributions::{Alphanumeric, Distribution, Standard, Uniform}; |
| 213 | + use crate::Rng; |
| 214 | + |
| 215 | + #[test] |
| 216 | + fn test_distributions_iter() { |
| 217 | + use crate::distributions::Open01; |
| 218 | + let mut rng = crate::test::rng(210); |
| 219 | + let distr = Open01; |
| 220 | + let mut iter = Distribution::<f32>::sample_iter(distr, &mut rng); |
| 221 | + let mut sum: f32 = 0.; |
| 222 | + for _ in 0..100 { |
| 223 | + sum += iter.next().unwrap(); |
| 224 | + } |
| 225 | + assert!(0. < sum && sum < 100.); |
| 226 | + } |
| 227 | + |
| 228 | + #[test] |
| 229 | + fn test_distributions_map() { |
| 230 | + let dist = Uniform::new_inclusive(0, 5).map(|val| val + 15); |
| 231 | + |
| 232 | + let mut rng = crate::test::rng(212); |
| 233 | + let val = dist.sample(&mut rng); |
| 234 | + assert!(val >= 15 && val <= 20); |
| 235 | + } |
| 236 | + |
| 237 | + #[test] |
| 238 | + fn test_make_an_iter() { |
| 239 | + fn ten_dice_rolls_other_than_five<R: Rng>( |
| 240 | + rng: &mut R, |
| 241 | + ) -> impl Iterator<Item = i32> + '_ { |
| 242 | + Uniform::new_inclusive(1, 6) |
| 243 | + .sample_iter(rng) |
| 244 | + .filter(|x| *x != 5) |
| 245 | + .take(10) |
| 246 | + } |
| 247 | + |
| 248 | + let mut rng = crate::test::rng(211); |
| 249 | + let mut count = 0; |
| 250 | + for val in ten_dice_rolls_other_than_five(&mut rng) { |
| 251 | + assert!((1..=6).contains(&val) && val != 5); |
| 252 | + count += 1; |
| 253 | + } |
| 254 | + assert_eq!(count, 10); |
| 255 | + } |
| 256 | + |
| 257 | + #[test] |
| 258 | + #[cfg(feature = "alloc")] |
| 259 | + fn test_dist_string() { |
| 260 | + use core::str; |
| 261 | + use crate::distributions::DistString; |
| 262 | + let mut rng = crate::test::rng(213); |
| 263 | + |
| 264 | + let s1 = Alphanumeric.sample_string(&mut rng, 20); |
| 265 | + assert_eq!(s1.len(), 20); |
| 266 | + assert_eq!(str::from_utf8(s1.as_bytes()), Ok(s1.as_str())); |
| 267 | + |
| 268 | + let s2 = Standard.sample_string(&mut rng, 20); |
| 269 | + assert_eq!(s2.chars().count(), 20); |
| 270 | + assert_eq!(str::from_utf8(s2.as_bytes()), Ok(s2.as_str())); |
| 271 | + } |
| 272 | +} |
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