Module rand::distributions::uniform

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A distribution uniformly sampling numbers within a given range.

Uniform is the standard distribution to sample uniformly from a range; e.g. Uniform::new_inclusive(1, 6).unwrap() can sample integers from 1 to 6, like a standard die. Rng::gen_range supports any type supported by Uniform.

This distribution is provided with support for several primitive types (all integer and floating-point types) as well as std::time::Duration, and supports extension to user-defined types via a type-specific back-end implementation.

The types UniformInt, UniformFloat and UniformDuration are the back-ends supporting sampling from primitive integer and floating-point ranges as well as from std::time::Duration; these types do not normally need to be used directly (unless implementing a derived back-end).

§Example usage

use rand::{Rng, thread_rng};
use rand::distributions::Uniform;

let mut rng = thread_rng();
let side = Uniform::new(-10.0, 10.0).unwrap();

// sample between 1 and 10 points
for _ in 0..rng.gen_range(1..=10) {
    // sample a point from the square with sides -10 - 10 in two dimensions
    let (x, y) = (rng.sample(side), rng.sample(side));
    println!("Point: {}, {}", x, y);

§Extending Uniform to support a custom type

To extend Uniform to support your own types, write a back-end which implements the UniformSampler trait, then implement the SampleUniform helper trait to “register” your back-end. See the MyF32 example below.

At a minimum, the back-end needs to store any parameters needed for sampling (e.g. the target range) and implement new, new_inclusive and sample. Those methods should include an assertion to check the range is valid (i.e. low < high). The example below merely wraps another back-end.

The new, new_inclusive and sample_single functions use arguments of type SampleBorrow<X> to support passing in values by reference or by value. In the implementation of these functions, you can choose to simply use the reference returned by SampleBorrow::borrow, or you can choose to copy or clone the value, whatever is appropriate for your type.

use rand::prelude::*;
use rand::distributions::uniform::{Uniform, SampleUniform,
        UniformSampler, UniformFloat, SampleBorrow, Error};

struct MyF32(f32);

#[derive(Clone, Copy, Debug)]
struct UniformMyF32(UniformFloat<f32>);

impl UniformSampler for UniformMyF32 {
    type X = MyF32;

    fn new<B1, B2>(low: B1, high: B2) -> Result<Self, Error>
        where B1: SampleBorrow<Self::X> + Sized,
              B2: SampleBorrow<Self::X> + Sized
        UniformFloat::<f32>::new(low.borrow().0, high.borrow().0).map(UniformMyF32)
    fn new_inclusive<B1, B2>(low: B1, high: B2) -> Result<Self, Error>
        where B1: SampleBorrow<Self::X> + Sized,
              B2: SampleBorrow<Self::X> + Sized
        UniformFloat::<f32>::new_inclusive(low.borrow().0, high.borrow().0).map(UniformMyF32)
    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {

impl SampleUniform for MyF32 {
    type Sampler = UniformMyF32;

let (low, high) = (MyF32(17.0f32), MyF32(22.0f32));
let uniform = Uniform::new(low, high).unwrap();
let x = uniform.sample(&mut thread_rng());




  • Helper trait similar to Borrow but implemented only for SampleUniform and references to SampleUniform in order to resolve ambiguity issues.
  • Range that supports generating a single sample efficiently.
  • Helper trait for creating objects using the correct implementation of UniformSampler for the sampling type.
  • Helper trait handling actual uniform sampling.