Function peroxide::macros::matlab_macro::random
source · pub fn random<T>() -> Twhere
Standard: Distribution<T>,
Expand description
Generates a random value using the thread-local random number generator.
This is simply a shortcut for thread_rng().gen()
. See thread_rng
for
documentation of the entropy source and Standard
for documentation of
distributions and type-specific generation.
§Provided implementations
The following types have provided implementations that generate values with the following ranges and distributions:
- Integers (
i32
,u32
,isize
,usize
, etc.): Uniformly distributed over all values of the type. char
: Uniformly distributed over all Unicode scalar values, i.e. all code points in the range0...0x10_FFFF
, except for the range0xD800...0xDFFF
(the surrogate code points). This includes unassigned/reserved code points.bool
: Generatesfalse
ortrue
, each with probability 0.5.- Floating point types (
f32
andf64
): Uniformly distributed in the half-open range[0, 1)
. See notes below. - Wrapping integers (
Wrapping<T>
), besides the type identical to their normal integer variants.
Also supported is the generation of the following compound types where all component types are supported:
- Tuples (up to 12 elements): each element is generated sequentially.
- Arrays (up to 32 elements): each element is generated sequentially;
see also
Rng::fill
which supports arbitrary array length for integer types and tends to be faster foru32
and smaller types. Option<T>
first generates abool
, and if true generates and returnsSome(value)
wherevalue: T
, otherwise returningNone
.
§Examples
let x = rand::random::<u8>();
println!("{}", x);
let y = rand::random::<f64>();
println!("{}", y);
if rand::random() { // generates a boolean
println!("Better lucky than good!");
}
If you’re calling random()
in a loop, caching the generator as in the
following example can increase performance.
use rand::Rng;
let mut v = vec![1, 2, 3];
for x in v.iter_mut() {
*x = rand::random()
}
// can be made faster by caching thread_rng
let mut rng = rand::thread_rng();
for x in v.iter_mut() {
*x = rng.gen();
}