Struct nannou::rand::distributions::Gamma

pub struct Gamma { /* fields omitted */ }

The Gamma distribution Gamma(shape, scale) distribution.

The density function of this distribution is

f(x) =  x^(k - 1) * exp(-x / θ) / (Γ(k) * θ^k)

where Γ is the Gamma function, k is the shape and θ is the scale and both k and θ are strictly positive.

The algorithm used is that described by Marsaglia & Tsang 2000¹, falling back to directly sampling from an Exponential for shape == 1, and using the boosting technique described in that paper for shape < 1.

Example

use rand::distributions::{Distribution, Gamma};

let gamma = Gamma::new(2.0, 5.0);
let v = gamma.sample(&mut rand::thread_rng());
println!("{} is from a Gamma(2, 5) distribution", v);

---

1. George Marsaglia and Wai Wan Tsang. 2000. "A Simple Method for Generating Gamma Variables" ACM Trans. Math. Softw. 26, 3 (September 2000), 363-372. DOI:10.1145/358407.358414 ↩

Methods

impl Gamma

pub fn new(shape: f64, scale: f64) -> Gamma

Construct an object representing the Gamma(shape, scale) distribution.

Panics if shape <= 0 or scale <= 0.

Trait Implementations

impl Debug for Gamma

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

Formats the value using the given formatter.

impl Distribution<f64> for Gamma

fn sample<R>(&self, rng: &mut R) -> f64 where
    R: Rng + ?Sized, 

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

Important traits for DistIter<'a, D, R, T>

impl<'a, D, R, T> Iterator for DistIter<'a, D, R, T> where
    D: Distribution<T>,
    R: Rng + 'a,  type Item = T;

fn sample_iter<R>(&'a self, rng: &'a mut R) -> DistIter<'a, Self, R, T> where
    R: Rng, 

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

impl Copy for Gamma

impl Clone for Gamma

fn clone(&self) -> Gamma

Returns a copy of the value.

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

Performs copy-assignment from source.