Trait Distribution

trait Distribution<T>

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

fn sample<R: Rng + ?Sized>(self: &Self, rng: &mut R) -> T

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

Provided Methods

fn sample_iter<R>(self: Self, rng: R) -> Iter<Self, R, T>
where
    R: Rng,
    Self: Sized

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::distr::{Distribution, Alphanumeric, Uniform, StandardUniform};

let mut rng = rand::rng();

// Vec of 16 x f32:
let v: Vec<f32> = StandardUniform.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).unwrap();
let mut roll_die = die_range.sample_iter(&mut rng);
while roll_die.next().unwrap() != 6 {
    println!("Not a 6; rolling again!");
}

fn map<F, S>(self: Self, func: F) -> Map<Self, F, T, S>
where
    F: Fn(T) -> S,
    Self: Sized

Map sampled values to type S

Example

use rand::distr::{Distribution, Uniform};

let die = Uniform::new_inclusive(1, 6).unwrap();
let even_number = die.map(|num| num % 2 == 0);
while !even_number.sample(&mut rand::rng()) {
    println!("Still odd; rolling again!");
}

Implementors