Trait peroxide::statistics::rand::Distribution
source · pub trait Distribution<T> {
fn sample<R>(&self, rng: &mut R) -> T
where
R: Rng + ?Sized;
fn sample_iter<R, 'a>(&'a self, rng: &'a mut R) -> DistIter<'a, Self, R, T>
where
Self: Sized,
R: Rng,
{ ... }
}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.
Required Methods§
Provided Methods§
sourcefn sample_iter<R, 'a>(&'a self, rng: &'a mut R) -> DistIter<'a, Self, R, T>where
Self: Sized,
R: Rng,
fn sample_iter<R, 'a>(&'a self, rng: &'a mut R) -> DistIter<'a, Self, R, T>where
Self: Sized,
R: Rng,
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!");
}