Struct rand::distributions::uniform::Uniform
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pub struct Uniform<X: SampleUniform> { /* fields omitted */ }
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.
Uniform::sample_single
instead samples directly from the given range,
and (depending on the back-end) may be faster when sampling a very small
number of values or only a single value from this range.
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 should attempt to sample in [low, high)
for
Uniform::new(low, high)
, i.e., excluding high
, but this may be very
difficult. All the primitive integer types satisfy this property, and the
float types normally satisfy it, but rounding may mean high
can occur.
Example
use rand::distributions::{Distribution, Uniform}; fn main() { 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); }
Methods
impl<X: SampleUniform> Uniform<X>
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pub fn new(low: X, high: X) -> Uniform<X>
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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(low: X, high: X) -> Uniform<X>
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Create a new Uniform
instance which samples uniformly from the closed
range [low, high]
(inclusive). Panics if low > high
.
pub fn sample_single<R: Rng + ?Sized>(low: X, high: X, rng: &mut R) -> X
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Sample a single value uniformly from [low, high)
.
Panics if low >= high
.
Trait Implementations
impl<X: Clone + SampleUniform> Clone for Uniform<X> where
X::Sampler: Clone,
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X::Sampler: Clone,
fn clone(&self) -> Uniform<X>
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Returns a copy of the value. Read more
fn clone_from(&mut self, source: &Self)
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Performs copy-assignment from source
. Read more
impl<X: Copy + SampleUniform> Copy for Uniform<X> where
X::Sampler: Copy,
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X::Sampler: Copy,
impl<X: Debug + SampleUniform> Debug for Uniform<X> where
X::Sampler: Debug,
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X::Sampler: Debug,
fn fmt(&self, __arg_0: &mut Formatter) -> Result
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Formats the value using the given formatter. Read more
impl<X: SampleUniform> Distribution<X> for Uniform<X>
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fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> X
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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>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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Self: Sized,
R: Rng,
Create an iterator that generates random values of T
, using rng
as the source of randomness. Read more
impl<X: SampleUniform> From<Range<X>> for Uniform<X>
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impl<T: SampleRange> Sample<T> for Range<T>
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fn sample<R: Rng>(&mut self, rng: &mut R) -> T
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: use Distribution instead
Generate a random value of Support
, using rng
as the source of randomness. Read more
impl<T: SampleRange> IndependentSample<T> for Range<T>
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fn ind_sample<R: Rng>(&self, rng: &mut R) -> T
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: use Distribution instead
Generate a random value.
Auto Trait Implementations
impl<X> Send for Uniform<X> where
<X as SampleUniform>::Sampler: Send,
<X as SampleUniform>::Sampler: Send,
impl<X> Sync for Uniform<X> where
<X as SampleUniform>::Sampler: Sync,
<X as SampleUniform>::Sampler: Sync,