Trait cap_rand::distributions::Distribution
source · pub trait Distribution<T> {
// Required method
fn sample<R>(&self, rng: &mut R) -> T
where R: Rng + ?Sized;
// Provided methods
fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> ⓘ
where R: Rng,
Self: Sized { ... }
fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>
where 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§
Provided Methods§
sourcefn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> ⓘwhere
R: Rng,
Self: Sized,
fn sample_iter<R>(self, rng: R) -> DistIter<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::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!");
}sourcefn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>where
F: Fn(T) -> S,
Self: Sized,
fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>where F: Fn(T) -> S, Self: Sized,
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!");
}