# Struct rand::distributions::Uniform

source · [−]`pub struct Uniform<X: SampleUniform>(_);`

## Expand description

Sample values uniformly between two bounds.

`Uniform::new`

and `Uniform::new_inclusive`

construct a uniform
distribution sampling from the given range; these functions may do extra
work up front to make sampling of multiple values faster. If only one sample
from the range is required, `Rng::gen_range`

can be more efficient.

When sampling from a constant range, many calculations can happen at
compile-time and all methods should be fast; for floating-point ranges and
the full range of integer types this should have comparable performance to
the `Standard`

distribution.

Steps are taken to avoid bias which might be present in naive
implementations; for example `rng.gen::<u8>() % 170`

samples from the range
`[0, 169]`

but is twice as likely to select numbers less than 85 than other
values. Further, the implementations here give more weight to the high-bits
generated by the RNG than the low bits, since with some RNGs the low-bits
are of lower quality than the high bits.

Implementations must sample in `[low, high)`

range for
`Uniform::new(low, high)`

, i.e., excluding `high`

. In particular, care must
be taken to ensure that rounding never results values `< low`

or `>= high`

.

## Example

```
use rand::distributions::{Distribution, Uniform};
let between = Uniform::from(10..10000);
let mut rng = rand::thread_rng();
let mut sum = 0;
for _ in 0..1000 {
sum += between.sample(&mut rng);
}
println!("{}", sum);
```

For a single sample, `Rng::gen_range`

may be preferred:

```
use rand::Rng;
let mut rng = rand::thread_rng();
println!("{}", rng.gen_range(0..10));
```

## Implementations

source### impl<X: SampleUniform> Uniform<X>

### impl<X: SampleUniform> Uniform<X>

source#### pub fn new<B1, B2>(low: B1, high: B2) -> Uniform<X>where

B1: SampleBorrow<X> + Sized,

B2: SampleBorrow<X> + Sized,

#### pub fn new<B1, B2>(low: B1, high: B2) -> Uniform<X>where

B1: SampleBorrow<X> + Sized,

B2: SampleBorrow<X> + Sized,

Create a new `Uniform`

instance which samples uniformly from the half
open range `[low, high)`

(excluding `high`

). Panics if `low >= high`

.

source#### pub fn new_inclusive<B1, B2>(low: B1, high: B2) -> Uniform<X>where

B1: SampleBorrow<X> + Sized,

B2: SampleBorrow<X> + Sized,

#### pub fn new_inclusive<B1, B2>(low: B1, high: B2) -> Uniform<X>where

B1: SampleBorrow<X> + Sized,

B2: SampleBorrow<X> + Sized,

Create a new `Uniform`

instance which samples uniformly from the closed
range `[low, high]`

(inclusive). Panics if `low > high`

.

## Trait Implementations

source### impl<X: SampleUniform> Distribution<X> for Uniform<X>

### impl<X: SampleUniform> Distribution<X> for Uniform<X>

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

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

`T`

, using `rng`

as the source of randomness.source#### fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>ⓘNotable traits for DistIter<D, R, T>`impl<D, R, T> Iterator for DistIter<D, R, T>where`

D: Distribution<T>,

R: Rng, type Item = T;

where

R: Rng,

Self: Sized,

#### fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>ⓘNotable traits for DistIter<D, R, T>`impl<D, R, T> Iterator for DistIter<D, R, T>where`

D: Distribution<T>,

R: Rng, type Item = T;

where

R: Rng,

Self: Sized,

D: Distribution<T>,

R: Rng, type Item = T;

`T`

, using `rng`

as
the source of randomness. Read more