pub struct SmallRng(/* private fields */);
Expand description
A small-state, fast non-crypto PRNG
SmallRng
may be a good choice when a PRNG with small state, cheap
initialization, good statistical quality and good performance are required.
Note that depending on the application, StdRng
may be faster on many
modern platforms while providing higher-quality randomness. Furthermore,
SmallRng
is not a good choice when:
- Security against prediction is important. Use
StdRng
instead. - Seeds with many zeros are provided. In such cases, it takes
SmallRng
about 10 samples to produce 0 and 1 bits with equal probability. Either provide seeds with an approximately equal number of 0 and 1 (for example by usingSeedableRng::from_entropy
orSeedableRng::seed_from_u64
), or useStdRng
instead.
The algorithm is deterministic but should not be considered reproducible due to dependence on platform and possible replacement in future library versions. For a reproducible generator, use a named PRNG from an external crate, e.g. rand_xoshiro or rand_chacha. Refer also to The Book.
The PRNG algorithm in SmallRng
is chosen to be efficient on the current
platform, without consideration for cryptography or security. The size of
its state is much smaller than StdRng
. The current algorithm is
Xoshiro256PlusPlus
on 64-bit platforms and Xoshiro128PlusPlus
on 32-bit
platforms. Both are also implemented by the rand_xoshiro crate.
§Examples
Initializing SmallRng
with a random seed can be done using SeedableRng::from_entropy
:
use rand::{Rng, SeedableRng};
use rand::rngs::SmallRng;
// Create small, cheap to initialize and fast RNG with a random seed.
// The randomness is supplied by the operating system.
let mut small_rng = SmallRng::from_entropy();
When initializing a lot of SmallRng
’s, using thread_rng
can be more
efficient:
use rand::{SeedableRng, thread_rng};
use rand::rngs::SmallRng;
// Create a big, expensive to initialize and slower, but unpredictable RNG.
// This is cached and done only once per thread.
let mut thread_rng = thread_rng();
// Create small, cheap to initialize and fast RNGs with random seeds.
// One can generally assume this won't fail.
let rngs: Vec<SmallRng> = (0..10)
.map(|_| SmallRng::from_rng(&mut thread_rng).unwrap())
.collect();
Trait Implementations§
source§impl RngCore for SmallRng
impl RngCore for SmallRng
source§fn fill_bytes(&mut self, dest: &mut [u8])
fn fill_bytes(&mut self, dest: &mut [u8])
dest
with random data. Read moresource§impl SeedableRng for SmallRng
impl SeedableRng for SmallRng
source§type Seed = <Xoshiro256PlusPlus as SeedableRng>::Seed
type Seed = <Xoshiro256PlusPlus as SeedableRng>::Seed
u8
arrays (we recommend [u8; N]
for some N
). Read moresource§fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error>
fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error>
Rng
. Read moresource§fn seed_from_u64(state: u64) -> Self
fn seed_from_u64(state: u64) -> Self
u64
seed. Read moresource§fn from_entropy() -> Self
fn from_entropy() -> Self
impl Eq for SmallRng
impl StructuralPartialEq for SmallRng
Auto Trait Implementations§
impl Freeze for SmallRng
impl RefUnwindSafe for SmallRng
impl Send for SmallRng
impl Sync for SmallRng
impl Unpin for SmallRng
impl UnwindSafe for SmallRng
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
source§impl<T> CloneToUninit for Twhere
T: Clone,
impl<T> CloneToUninit for Twhere
T: Clone,
source§unsafe fn clone_to_uninit(&self, dst: *mut T)
unsafe fn clone_to_uninit(&self, dst: *mut T)
clone_to_uninit
)source§impl<R> Rng for R
impl<R> Rng for R
source§fn gen<T>(&mut self) -> Twhere
Standard: Distribution<T>,
fn gen<T>(&mut self) -> Twhere
Standard: Distribution<T>,
source§fn gen_range<T, R>(&mut self, range: R) -> Twhere
T: SampleUniform,
R: SampleRange<T>,
fn gen_range<T, R>(&mut self, range: R) -> Twhere
T: SampleUniform,
R: SampleRange<T>,
source§fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T
fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T
source§fn sample_iter<T, D>(self, distr: D) -> DistIter<D, Self, T> ⓘwhere
D: Distribution<T>,
Self: Sized,
fn sample_iter<T, D>(self, distr: D) -> DistIter<D, Self, T> ⓘwhere
D: Distribution<T>,
Self: Sized,
source§fn gen_bool(&mut self, p: f64) -> bool
fn gen_bool(&mut self, p: f64) -> bool
p
of being true. Read moresource§fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool
fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool
numerator/denominator
of being
true. I.e. gen_ratio(2, 3)
has chance of 2 in 3, or about 67%, of
returning true. If numerator == denominator
, then the returned value
is guaranteed to be true
. If numerator == 0
, then the returned
value is guaranteed to be false
. Read more