Struct rand::distributions::Bernoulli

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pub struct Bernoulli { /* fields omitted */ }

[−] Expand description

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);
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.

Methods

impl Bernoulli

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pub fn new(p: f64) -> Bernoulli

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

Panics

If p < 0 or p > 1.

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.)

Trait Implementations

impl Clone for Bernoulli

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fn clone(&self) -> Bernoulli

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Returns a copy of the value.

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

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Performs copy-assignment from source.

impl Copy for Bernoulli

impl Debug for Bernoulli

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

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Formats the value using the given formatter.

impl Distribution<bool> for Bernoulli

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

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

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