Struct rand::distributions::Bernoulli

pub struct Bernoulli { /* fields omitted */ }

The Bernoulli distribution.

This is a special case of the Binomial distribution where n = 1.

Example

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

let d = Bernoulli::new(0.3).unwrap();
let v = d.sample(&mut rand::thread_rng());
println!("{} is from a Bernoulli distribution", v);

Precision

This Bernoulli distribution uses 64 bits from the RNG (a u64), so only probabilities that are multiples of 2^-64 can be represented.

Implementations

impl Bernoulli

pub fn new(p: f64) -> Result<Bernoulli, BernoulliError>

Construct a new Bernoulli with the given probability of success p.

Precision

For p = 1.0, the resulting distribution will always generate true. For p = 0.0, the resulting distribution will always generate false.

This method is accurate for any input p in the range [0, 1] which is a multiple of 2^-64. (Note that not all multiples of 2^-64 in [0, 1] can be represented as a f64.)

pub fn from_ratio(
    numerator: u32,
    denominator: u32
) -> Result<Bernoulli, BernoulliError>

Construct a new Bernoulli with the probability of success of numerator-in-denominator. I.e. new_ratio(2, 3) will return a Bernoulli with a 2-in-3 chance, or about 67%, of returning true.

return true. If numerator == 0 it will always return false. For numerator > denominator and denominator == 0, this returns an error. Otherwise, for numerator == denominator, samples are always true; for numerator == 0 samples are always false.

Trait Implementations

impl Clone for Bernoulli

fn clone(&self) -> Bernoulli

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Copy for Bernoulli

impl Debug for Bernoulli

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

Formats the value using the given formatter.

impl Distribution<bool> for Bernoulli

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

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.