`pub struct Bernoulli { /* private fields */ }`

## Expand description

The Bernoulli distribution `Bernoulli(p)`

.

This distribution describes a single boolean random variable, which is true
with probability `p`

and false with probability `1 - p`

.
It is a special case of the Binomial distribution with `n = 1`

.

## §Plot

The following plot shows the Bernoulli distribution with `p = 0.1`

,
`p = 0.5`

, and `p = 0.9`

.

## §Example

```
use rand::distr::{Bernoulli, Distribution};
let d = Bernoulli::new(0.3).unwrap();
let v = d.sample(&mut rand::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§

Source§### impl Bernoulli

### impl Bernoulli

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

#### 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`

.)

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

#### 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§

Source§### impl<'de> Deserialize<'de> for Bernoulli

### impl<'de> Deserialize<'de> for Bernoulli

Source§#### fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,

#### fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,

Source§### impl Distribution<bool> for Bernoulli

### impl Distribution<bool> for Bernoulli

### impl Copy for Bernoulli

### impl StructuralPartialEq for Bernoulli

## Auto Trait Implementations§

### impl Freeze for Bernoulli

### impl RefUnwindSafe for Bernoulli

### impl Send for Bernoulli

### impl Sync for Bernoulli

### impl Unpin for Bernoulli

### impl UnwindSafe for Bernoulli

## Blanket Implementations§

Source§### impl<T> BorrowMut<T> for Twhere
T: ?Sized,

### impl<T> BorrowMut<T> for Twhere
T: ?Sized,

Source§#### fn borrow_mut(&mut self) -> &mut T

#### fn borrow_mut(&mut self) -> &mut T

Source§### impl<T> CloneToUninit for Twhere
T: Clone,

### impl<T> CloneToUninit for Twhere
T: Clone,

Source§#### unsafe fn clone_to_uninit(&self, dst: *mut T)

#### unsafe fn clone_to_uninit(&self, dst: *mut T)

`clone_to_uninit`

)