Trait RngExt
pub trait RngExt: Rng
User-level interface for RNGs
Rng is the dyn-safe implementation-level interface for Random
(Number) Generators. This trait, RngExt, provides a user-level interface
on RNGs. It is implemented automatically for any R: [Rng][].
This trait must usually be brought into scope via use rand::RngExt; or
use rand::prelude::*;.
Generic usage
The basic pattern is fn foo<R: Rng + ?Sized>(rng: &mut R). Some
things are worth noting here:
- Since
RngExt: Rngand everyRngExtimplementsRng, it makes no difference whether we useR: RngorR: RngExtforR: Sized. - Only
Rngis dyn safe, supporting&mut dyn RngandR: Rng + ?Sized.
An alternative pattern is possible: fn foo<R: Rng>(rng: R). This has some
trade-offs. It allows the argument to be consumed directly without a &mut;
also it still works directly
on references (including type-erased references). Unfortunately within the
function foo it is not known whether rng is a reference type or not,
hence many uses of rng require an extra reference, either explicitly
(distr.sample(&mut rng)) or implicitly (rng.random()); one may hope the
optimiser can remove redundant references later.
Example:
use ;
Sized>
# let v = foo;
Provided Methods
fn random<T>(&mut self) -> T where StandardUniform: Distribution<T>,Return a random value via the
StandardUniformdistribution.Example
use RngExt; let mut rng = rng; let x: u32 = rng.random; println!; println!;Arrays and tuples
The
rng.random()method is able to generate arrays and tuples (up to 12 elements), so long as all element types can be generated.For arrays of integers, especially for those with small element types (< 64 bit), it will likely be faster to instead use
RngExt::fill, though note that generated values will differ.use RngExt; let mut rng = rng; let tuple: = rng.random; // arbitrary tuple support let arr1: = rng.random; // array construction let mut arr2 = ; rng.fill; // array fillfn random_iter<T>(self) -> Iter<StandardUniform, Self, T> where Self: Sized, StandardUniform: Distribution<T>,Return an iterator over
randomvariatesThis is a just a wrapper over
RngExt::sample_iterusingdistr::StandardUniform.Note: this method consumes its argument. Use
(&mut rng).random_iter()to avoid consuming the RNG.Example
use ; let rng = seed_from_u64; let v: = rng.random_iter.take.collect; assert_eq!;fn random_range<T, R>(&mut self, range: R) -> T where T: SampleUniform, R: SampleRange<T>,Generate a random value in the given range.
This function is optimised for the case that only a single sample is made from the given range. See also the
Uniformdistribution type which may be faster if sampling from the same range repeatedly.All supported types may be sampled with
low..high_exclusive(Range) andlow..=high(RangeInclusive) syntax. Unsigned integer types also support..high_exclusive(RangeTo) and..=high(RangeToInclusive) syntax.Panics
Panics if the range is empty, or if
high - lowoverflows for floats.Example
use RngExt; let mut rng = rng; // Exclusive range println!; println!; // Inclusive range println!; println!;fn random_bool(&mut self, p: f64) -> boolReturn a bool with a probability
pof being true.See also the
Bernoullidistribution, which may be faster if sampling from the same probability repeatedly.Example
use RngExt; let mut rng = rng; println!;Panics
If
p < 0orp > 1.fn random_ratio(&mut self, numerator: u32, denominator: u32) -> boolReturn a bool with a probability of
numerator/denominatorof being true.That is,
random_ratio(2, 3)has chance of 2 in 3, or about 67%, of returning true. Ifnumerator == denominator, then the returned value is guaranteed to betrue. Ifnumerator == 0, then the returned value is guaranteed to befalse.See also the
Bernoullidistribution, which may be faster if sampling from the samenumeratoranddenominatorrepeatedly.Panics
If
denominator == 0ornumerator > denominator.Example
use RngExt; let mut rng = rng; println!;fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> TSample a new value, using the given distribution.
Example
use RngExt; use Uniform; let mut rng = rng; let x = rng.sample; // Type annotation requires two types, the type and distribution; the // distribution can be inferred. let y = rng.;fn sample_iter<T, D>(self, distr: D) -> Iter<D, Self, T> where D: Distribution<T>, Self: Sized,Create an iterator that generates values using the given distribution.
Note: this method consumes its arguments. Use
(&mut rng).sample_iter(..)to avoid consuming the RNG.Example
use RngExt; use ; let mut rng = rng; // Vec of 16 x f32: let v: = .sample_iter.take.collect; // String: let s: String = .sample_iter .take .map .collect; // Combined values println!; // Dice-rolling: let die_range = new_inclusive.unwrap; let mut roll_die = .sample_iter; while roll_die.next.unwrap != 6fn fill<T: Fill>(&mut self, dest: &mut [T])Fill any type implementing
Fillwith random dataThis method is implemented for types which may be safely reinterpreted as an (aligned)
[u8]slice then filled with random data. It is often faster than usingRngExt::randombut not value-equivalent.The distribution is expected to be uniform with portable results, but this cannot be guaranteed for third-party implementations.
Example
use RngExt; let mut arr = ; rng.fill;
Implementors
impl<R> RngExt for SmallRng where R: Rng + ?Sized,impl<R> RngExt for StdRng where R: Rng + ?Sized,impl<R> RngExt for ThreadRng where R: Rng + ?Sized,impl<R> RngExt for Xoshiro128PlusPlus where R: Rng + ?Sized,impl<R> RngExt for Xoshiro256PlusPlus where R: Rng + ?Sized,impl<R: Rng + ?Sized> RngExt for R