Struct rand_distr::Gamma

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

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_distr::{Distribution, Gamma};

let gamma = Gamma::new(2.0, 5.0).unwrap();
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 ↩

Implementations

impl<F> Gamma<F>

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

pub fn new(shape: F, scale: F) -> Result<Gamma<F>, Error>

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

Trait Implementations

impl<F> Clone for Gamma<F>

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

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F> Debug for Gamma<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<F> for Gamma<F>

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

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

impl<F> Copy for Gamma<F>

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