Struct rand_distr::Dirichlet

pub struct Dirichlet<F>
where
    F: Float,
    StandardNormal: Distribution<F>,
    Exp1: Distribution<F>,
    Open01: Distribution<F>,
{ /* private fields */ }

The Dirichlet distribution Dirichlet(alpha).

The Dirichlet distribution is a family of continuous multivariate probability distributions parameterized by a vector alpha of positive reals. It is a multivariate generalization of the beta distribution.

Example

use rand::prelude::*;
use rand_distr::Dirichlet;

let dirichlet = Dirichlet::new(&[1.0, 2.0, 3.0]).unwrap();
let samples = dirichlet.sample(&mut rand::thread_rng());
println!("{:?} is from a Dirichlet([1.0, 2.0, 3.0]) distribution", samples);

Implementations

impl<F> Dirichlet<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

pub fn new(alpha: &[F]) -> Result<Dirichlet<F>, Error>

Construct a new Dirichlet with the given alpha parameter alpha.

Requires alpha.len() >= 2.

pub fn new_with_size(alpha: F, size: usize) -> Result<Dirichlet<F>, Error>

Construct a new Dirichlet with the given shape parameter alpha and size.

Requires size >= 2.

Trait Implementations

impl<F> Clone for Dirichlet<F>

where F: Float + Clone, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn clone(&self) -> Dirichlet<F>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<F> Debug for Dirichlet<F>

where F: Float + Debug, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<F> Distribution<Vec<F>> for Dirichlet<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

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

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

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.

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