pub trait Distribution<T> {
    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T;

    fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>Notable traits for DistIter<D, R, T>impl<D, R, T> Iterator for DistIter<D, R, T> where
    D: Distribution<T>,
    R: Rng
type Item = T;

        R: Rng,
        Self: Sized
, { ... }
fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>
        F: Fn(T) -> S,
        Self: Sized
, { ... } }
Expand description

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 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.

Required methods

Generate a random value of T, using rng as the source of randomness.

Provided methods

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.

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)

// 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!");

Create a distribution of values of ‘S’ by mapping the output of Self through the closure F

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!");

Implementations on Foreign Types