Struct rand_distr::Dirichlet

pub struct Dirichlet<N> { /* fields omitted */ }

The dirichelet 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(vec![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);

Methods

impl<N: Float> Dirichlet<N> where
    StandardNormal: Distribution<N>,
    Exp1: Distribution<N>,
    Open01: Distribution<N>, 

pub fn new<V: Into<Vec<N>>>(alpha: V) -> Result<Dirichlet<N>, Error>

Construct a new Dirichlet with the given alpha parameter alpha.

Requires alpha.len() >= 2.

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

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

Requires size >= 2.

Trait Implementations

impl<N: Clone> Clone for Dirichlet<N>

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<N: Debug> Debug for Dirichlet<N>

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

Formats the value using the given formatter.

impl<N: Float> Distribution<Vec<N>> for Dirichlet<N> where
    StandardNormal: Distribution<N>,
    Exp1: Distribution<N>,
    Open01: Distribution<N>, 

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

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

Important traits for DistIter<D, R, T>

impl<D, R, T> Iterator for DistIter<D, R, T> where
    D: Distribution<T>,
    R: Rng,  type Item = T;

fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> where
    R: Rng, 

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