Struct rand_distr::Hypergeometric

pub struct Hypergeometric { /* private fields */ }

The hypergeometric distribution Hypergeometric(N, K, n).

This is the distribution of successes in samples of size n drawn without replacement from a population of size N containing K success states. It has the density function: f(k) = binomial(K, k) * binomial(N-K, n-k) / binomial(N, n), where binomial(a, b) = a! / (b! * (a - b)!).

The binomial distribution is the analogous distribution for sampling with replacement. It is a good approximation when the population size is much larger than the sample size.

Example

use rand_distr::{Distribution, Hypergeometric};

let hypergeo = Hypergeometric::new(60, 24, 7).unwrap();
let v = hypergeo.sample(&mut rand::thread_rng());
println!("{} is from a hypergeometric distribution", v);

Implementations

impl Hypergeometric

pub fn new(
    total_population_size: u64,
    population_with_feature: u64,
    sample_size: u64
) -> Result<Self, Error>

Constructs a new Hypergeometric with the shape parameters N = total_population_size, K = population_with_feature, n = sample_size.

Trait Implementations

impl Clone for Hypergeometric

fn clone(&self) -> Hypergeometric

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Hypergeometric

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

Formats the value using the given formatter.

impl Distribution<u64> for Hypergeometric

fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64

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,

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,

Create a distribution of values of ‘S’ by mapping the output of Self through the closure F

impl Copy for Hypergeometric