Trait rand::distributions::Distribution [−] [src]

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

    fn sample_iter<'a, R>(&'a self, rng: &'a mut R) -> DistIter<'a, Self, R, T>
    where
        Self: Sized,
        R: Rng,
    { ... }
}

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.

Required Methods

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

Provided Methods

ⓘImportant traits for DistIter<'a, D, R, T>

Create an iterator that generates random values of T, using rng as the source of randomness.

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).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!");
}

Implementations on Foreign Types

impl<'a, T, D: Distribution<T>> Distribution<T> for &'a D
[src]

[src]

ⓘImportant traits for DistIter<'a, D, R, T>
[src]

Implementors