Move Distribution trait and associates to sub-module

src/distributions/distribution.rs

@@ -0,0 +1,236 @@
+// 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.
+
+//! Distribution trait and associates
+
+use crate::Rng;
+use core::iter;
+
+/// Types (distributions) that can be used to create a random instance of `T`.
+///
+/// It is possible to sample from a distribution through both the
+/// `Distribution` and [`Rng`] traits, via `distr.sample(&mut rng)` and
+/// `rng.sample(distr)`. They also both offer the [`sample_iter`] method, which
+/// produces an iterator that samples from the distribution.
+///
+/// All implementations are expected to be immutable; this has the significant
+/// advantage of not needing to consider thread safety, and for most
+/// distributions efficient state-less sampling algorithms are available.
+///
+/// Implementations are typically expected to be portable with reproducible
+/// results when used with a PRNG with fixed seed; see the
+/// [portability chapter](https://rust-random.github.io/book/portability.html)
+/// of The Rust Rand Book. In some cases this does not apply, e.g. the `usize`
+/// type requires different sampling on 32-bit and 64-bit machines.
+///
+/// [`sample_iter`]: Distribution::sample_iter
+pub trait Distribution<T> {
+    /// Generate a random value of `T`, using `rng` as the source of randomness.
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T;
+
+    /// Create an iterator that generates random values of `T`, using `rng` as
+    /// the source of randomness.
+    ///
+    /// Note that this function takes `self` by value. This works since
+    /// `Distribution<T>` is impl'd for `&D` where `D: Distribution<T>`,
+    /// however borrowing is not automatic hence `distr.sample_iter(...)` may
+    /// need to be replaced with `(&distr).sample_iter(...)` to borrow or
+    /// `(&*distr).sample_iter(...)` to reborrow an existing reference.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::thread_rng;
+    /// use rand::distributions::{Distribution, Alphanumeric, Uniform, Standard};
+    ///
+    /// let mut rng = thread_rng();
+    ///
+    /// // Vec of 16 x f32:
+    /// let v: Vec<f32> = Standard.sample_iter(&mut rng).take(16).collect();
+    ///
+    /// // String:
+    /// let s: String = Alphanumeric
+    ///     .sample_iter(&mut rng)
+    ///     .take(7)
+    ///     .map(char::from)
+    ///     .collect();
+    ///
+    /// // Dice-rolling:
+    /// let die_range = Uniform::new_inclusive(1, 6);
+    /// let mut roll_die = die_range.sample_iter(&mut rng);
+    /// while roll_die.next().unwrap() != 6 {
+    ///     println!("Not a 6; rolling again!");
+    /// }
+    /// ```
+    fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>
+    where
+        R: Rng,
+        Self: Sized,
+    {
+        DistIter {
+            distr: self,
+            rng,
+            phantom: ::core::marker::PhantomData,
+        }
+    }
+
+    /// Create a distribution of values of 'S' by mapping the output of `Self`
+    /// through the closure `F`
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::thread_rng;
+    /// use rand::distributions::{Distribution, Uniform};
+    ///
+    /// let mut rng = thread_rng();
+    ///
+    /// let die = Uniform::new_inclusive(1, 6);
+    /// let even_number = die.map(|num| num % 2 == 0);
+    /// while !even_number.sample(&mut rng) {
+    ///     println!("Still odd; rolling again!");
+    /// }
+    /// ```
+    fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>
+    where
+        F: Fn(T) -> S,
+        Self: Sized,
+    {
+        DistMap {
+            distr: self,
+            func,
+            phantom: ::core::marker::PhantomData,
+        }
+    }
+}
+
+impl<'a, T, D: Distribution<T>> Distribution<T> for &'a D {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T {
+        (*self).sample(rng)
+    }
+}
+
+/// An iterator that generates random values of `T` with distribution `D`,
+/// using `R` as the source of randomness.
+///
+/// This `struct` is created by the [`sample_iter`] method on [`Distribution`].
+/// See its documentation for more.
+///
+/// [`sample_iter`]: Distribution::sample_iter
+#[derive(Debug)]
+pub struct DistIter<D, R, T> {
+    distr: D,
+    rng: R,
+    phantom: ::core::marker::PhantomData<T>,
+}
+
+impl<D, R, T> Iterator for DistIter<D, R, T>
+where
+    D: Distribution<T>,
+    R: Rng,
+{
+    type Item = T;
+
+    #[inline(always)]
+    fn next(&mut self) -> Option<T> {
+        // Here, self.rng may be a reference, but we must take &mut anyway.
+        // Even if sample could take an R: Rng by value, we would need to do this
+        // since Rng is not copyable and we cannot enforce that this is "reborrowable".
+        Some(self.distr.sample(&mut self.rng))
+    }
+
+    fn size_hint(&self) -> (usize, Option<usize>) {
+        (usize::max_value(), None)
+    }
+}
+
+impl<D, R, T> iter::FusedIterator for DistIter<D, R, T>
+where
+    D: Distribution<T>,
+    R: Rng,
+{
+}
+
+#[cfg(features = "nightly")]
+impl<D, R, T> iter::TrustedLen for DistIter<D, R, T>
+where
+    D: Distribution<T>,
+    R: Rng,
+{
+}
+
+/// A distribution of values of type `S` derived from the distribution `D`
+/// by mapping its output of type `T` through the closure `F`.
+///
+/// This `struct` is created by the [`Distribution::map`] method.
+/// See its documentation for more.
+#[derive(Debug)]
+pub struct DistMap<D, F, T, S> {
+    distr: D,
+    func: F,
+    phantom: ::core::marker::PhantomData<fn(T) -> S>,
+}
+
+impl<D, F, T, S> Distribution<S> for DistMap<D, F, T, S>
+where
+    D: Distribution<T>,
+    F: Fn(T) -> S,
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> S {
+        (self.func)(self.distr.sample(rng))
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::distributions::{Distribution, Uniform};
+    use crate::Rng;
+
+    #[test]
+    fn test_distributions_iter() {
+        use crate::distributions::Open01;
+        let mut rng = crate::test::rng(210);
+        let distr = Open01;
+        let mut iter = Distribution::<f32>::sample_iter(distr, &mut rng);
+        let mut sum: f32 = 0.;
+        for _ in 0..100 {
+            sum += iter.next().unwrap();
+        }
+        assert!(0. < sum && sum < 100.);
+    }
+
+    #[test]
+    fn test_distributions_map() {
+        let dist = Uniform::new_inclusive(0, 5).map(|val| val + 15);
+
+        let mut rng = crate::test::rng(212);
+        let val = dist.sample(&mut rng);
+        assert!(val >= 15 && val <= 20);
+    }
+
+    #[test]
+    fn test_make_an_iter() {
+        fn ten_dice_rolls_other_than_five<R: Rng>(
+            rng: &mut R,
+        ) -> impl Iterator<Item = i32> + '_ {
+            Uniform::new_inclusive(1, 6)
+                .sample_iter(rng)
+                .filter(|x| *x != 5)
+                .take(10)
+        }
+
+        let mut rng = crate::test::rng(211);
+        let mut count = 0;
+        for val in ten_dice_rolls_other_than_five(&mut rng) {
+            assert!((1..=6).contains(&val) && val != 5);
+            count += 1;
+        }
+        assert_eq!(count, 10);
+    }
+}