Struct rand::distributions::uniform::Uniform

pub struct Uniform<X: SampleUniform>(_);

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

impl<X: SampleUniform> Uniform<X>

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.

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

impl<X: Clone + SampleUniform> Clone for Uniform<X>where X::Sampler: Clone,

fn clone(&self) -> Uniform<X>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<X: Debug + SampleUniform> Debug for Uniform<X>where X::Sampler: Debug,

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

Formats the value using the given formatter.

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

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

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.

fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>where F: Fn(T) -> S, Self: Sized,

Create a distribution of values of ‘S’ by mapping the output of Self through the closure F

impl<X: SampleUniform> From<Range<X>> for Uniform<X>

fn from(r: Range<X>) -> Uniform<X>

Converts to this type from the input type.

impl<X: SampleUniform> From<RangeInclusive<X>> for Uniform<X>

fn from(r: RangeInclusive<X>) -> Uniform<X>

Converts to this type from the input type.

impl<X: PartialEq + SampleUniform> PartialEq<Uniform<X>> for Uniform<X>where X::Sampler: PartialEq,

fn eq(&self, other: &Uniform<X>) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<X: Copy + SampleUniform> Copy for Uniform<X>where X::Sampler: Copy,

impl<X: SampleUniform> StructuralPartialEq for Uniform<X>