Struct Dirichlet

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

The Dirichlet distribution Dirichlet(α₁, α₂, ..., αₖ).

The Dirichlet distribution is a family of continuous multivariate probability distributions parameterized by a vector of positive real numbers α₁, α₂, ..., αₖ, where k is the number of dimensions of the distribution. The distribution is supported on the k-1-dimensional simplex, which is the set of points x = [x₁, x₂, ..., xₖ] such that 0 ≤ xᵢ ≤ 1 and ∑ xᵢ = 1. It is a multivariate generalization of the Beta distribution. The distribution is symmetric when all αᵢ are equal.

Plot

The following plot illustrates the 2-dimensional simplices for various 3-dimensional Dirichlet distributions.

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::rng());
println!("{:?} is from a Dirichlet([1.0, 2.0, 3.0]) distribution", samples);

Implementations

impl<F, const N: usize> Dirichlet<F, N>

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

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

Construct a new Dirichlet with the given alpha parameter alpha.

Requires alpha.len() >= 2, and each value in alpha must be positive, finite and not subnormal.

Trait Implementations

impl<F, const N: usize> Clone for Dirichlet<F, N>

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

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F, const N: usize> Debug for Dirichlet<F, N>

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, const N: usize> Distribution<[F; N]> for Dirichlet<F, N>

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

fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> [F; N]

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

impl<F, const N: usize> PartialEq for Dirichlet<F, N>

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

fn eq(&self, other: &Dirichlet<F, N>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<F, const N: usize> StructuralPartialEq for Dirichlet<F, N>

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