Struct rand::rngs::SmallRng [−][src]
pub struct SmallRng(_);
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.
It is not a good choice when security against prediction or
reproducibility are important.
This PRNG is feature-gated: to use, you must enable the crate feature
small_rng
.
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_pcg 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 Pcg64Mcg
on 64-bit
platforms and Pcg32
on 32-bit platforms. Both are
implemented by the rand_pcg 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
Auto Trait Implementations
impl RefUnwindSafe for SmallRng
impl UnwindSafe for SmallRng
Blanket Implementations
Mutably borrows from an owned value. Read more
fn gen_range<T: SampleUniform, B1, B2>(&mut self, low: B1, high: B2) -> T where
B1: SampleBorrow<T> + Sized,
B2: SampleBorrow<T> + Sized,
[src]
fn gen_range<T: SampleUniform, B1, B2>(&mut self, low: B1, high: B2) -> T where
B1: SampleBorrow<T> + Sized,
B2: SampleBorrow<T> + Sized,
[src]Generate a random value in the range [low
, high
), i.e. inclusive of
low
and exclusive of high
. Read more
Sample a new value, using the given distribution. Read more
fn sample_iter<T, D>(self, distr: D) -> DistIter<D, Self, T>ⓘ where
D: Distribution<T>,
Self: Sized,
[src]
fn sample_iter<T, D>(self, distr: D) -> DistIter<D, Self, T>ⓘ where
D: Distribution<T>,
Self: Sized,
[src]Create an iterator that generates values using the given distribution. Read more
Fill dest
entirely with random bytes (uniform value distribution),
where dest
is any type supporting AsByteSliceMut
, namely slices
and arrays over primitive integer types (i8
, i16
, u32
, etc.). Read more
Fill dest
entirely with random bytes (uniform value distribution),
where dest
is any type supporting AsByteSliceMut
, namely slices
and arrays over primitive integer types (i8
, i16
, u32
, etc.). Read more
Return a bool with a probability p
of being true. Read more
Return a bool with a probability of 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