# distribution.rs - source
Source of the Rust file `/home/key/.cargo/registry/src/github.com-1ecc6299db9ec823/rand-0.8.5/src/distributions/distribution.rs`.
Keywords: rust, rustlang, rust-lang

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    // 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;
    #[cfg(feature = "alloc")]
    use alloc::string::String;

    /// 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))
        }
    }

    /// `String` sampler
    ///
    /// Sampling a `String` of random characters is not quite the same as collecting
    /// a sequence of chars. This trait contains some helpers.
    #[cfg(feature = "alloc")]
    pub trait DistString {
        /// Append `len` random chars to `string`
        fn append_string<R: Rng + ?Sized>(&self, rng: &mut R, string: &mut String, len: usize);

        /// Generate a `String` of `len` random chars
        #[inline]
        fn sample_string<R: Rng + ?Sized>(&self, rng: &mut R, len: usize) -> String {
            let mut s = String::new();
            self.append_string(rng, &mut s, len);
            s
        }
    }

    #[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!((15..=20).contains(&val));
        }

        #[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);
        }

        #[test]
        #[cfg(feature = "alloc")]
        fn test_dist_string() {
            use core::str;
            use crate::distributions::{Alphanumeric, DistString, Standard};
            let mut rng = crate::test::rng(213);

            let s1 = Alphanumeric.sample_string(&mut rng, 20);
            assert_eq!(s1.len(), 20);
            assert_eq!(str::from_utf8(s1.as_bytes()), Ok(s1.as_str()));

            let s2 = Standard.sample_string(&mut rng, 20);
            assert_eq!(s2.chars().count(), 20);
            assert_eq!(str::from_utf8(s2.as_bytes()), Ok(s2.as_str()));
        }
    }