# Trait rand::SeedableRng
[−]
[src]

pub trait SeedableRng { type Seed: Sized + Default + AsMut<[u8]>; fn from_seed(seed: Self::Seed) -> Self; fn from_rng<R>(rng: R) -> Result<Self, Error>

where

R: RngCore, { ... } }

A random number generator that can be explicitly seeded.

This trait encapsulates the low-level functionality common to all pseudo-random number generators (PRNGs, or algorithmic generators).

The `rand::FromEntropy`

trait is automatically implemented for every type
implementing `SeedableRng`

, providing a convenient `from_entropy()`

constructor.

## Associated Types

`type Seed: Sized + Default + AsMut<[u8]>`

Seed type, which is restricted to types mutably-dereferencable as `u8`

arrays (we recommend `[u8; N]`

for some `N`

).

It is recommended to seed PRNGs with a seed of at least circa 100 bits,
which means an array of `[u8; 12]`

or greater to avoid picking RNGs with
partially overlapping periods.

For cryptographic RNG's a seed of 256 bits is recommended, `[u8; 32]`

.

# Implementing `SeedableRng`

for RNGs with large seeds

Note that the required traits `core::default::Default`

and
`core::convert::AsMut<u8>`

are not implemented for large arrays
`[u8; N]`

with `N`

> 32. To be able to implement the traits required by
`SeedableRng`

for RNGs with such large seeds, the newtype pattern can be
used:

use rand_core::SeedableRng; const N: usize = 64; pub struct MyRngSeed(pub [u8; N]); pub struct MyRng(MyRngSeed); impl Default for MyRngSeed { fn default() -> MyRngSeed { MyRngSeed([0; N]) } } impl AsMut<[u8]> for MyRngSeed { fn as_mut(&mut self) -> &mut [u8] { &mut self.0 } } impl SeedableRng for MyRng { type Seed = MyRngSeed; fn from_seed(seed: MyRngSeed) -> MyRng { MyRng(seed) } }

## Required Methods

`fn from_seed(seed: Self::Seed) -> Self`

Create a new PRNG using the given seed.

PRNG implementations are allowed to assume that bits in the seed are well distributed. That means usually that the number of one and zero bits are about equal, and values like 0, 1 and (size - 1) are unlikely.

PRNG implementations are recommended to be reproducible. A PRNG seeded using this function with a fixed seed should produce the same sequence of output in the future and on different architectures (with for example different endianness).

It is however not required that this function yield the same state as a reference implementation of the PRNG given equivalent seed; if necessary another constructor replicating behaviour from a reference implementation can be added.

PRNG implementations should make sure `from_seed`

never panics. In the
case that some special values (like an all zero seed) are not viable
seeds it is preferable to map these to alternative constant value(s),
for example `0xBAD5EEDu32`

or `0x0DDB1A5E5BAD5EEDu64`

("odd biases? bad
seed"). This is assuming only a small number of values must be rejected.

## Provided Methods

`fn from_rng<R>(rng: R) -> Result<Self, Error> where`

R: RngCore,

R: RngCore,

Create a new PRNG seeded from another `Rng`

.

This is the recommended way to initialize PRNGs with fresh entropy. The
`FromEntropy`

trait provides a convenient `from_entropy`

method
based on `from_rng`

.

Usage of this method is not recommended when reproducibility is required since implementing PRNGs are not required to fix Endianness and are allowed to modify implementations in new releases.

It is important to use a good source of randomness to initialize the PRNG. Cryptographic PRNG may be rendered insecure when seeded from a non-cryptographic PRNG or with insufficient entropy. Many non-cryptographic PRNGs will show statistical bias in their first results if their seed numbers are small or if there is a simple pattern between them.

Prefer to seed from a strong external entropy source like `OsRng`

or
from a cryptographic PRNG; if creating a new generator for cryptographic
uses you *must* seed from a strong source.

Seeding a small PRNG from another small PRNG is possible, but something to be careful with. An extreme example of how this can go wrong is seeding an Xorshift RNG from another Xorshift RNG, which will effectively clone the generator. In general seeding from a generator which is hard to predict is probably okay.

PRNG implementations are allowed to assume that a good RNG is provided for seeding, and that it is cryptographically secure when appropriate.

## Implementations on Foreign Types

`impl<R> SeedableRng for BlockRng<R> where`

R: BlockRngCore + SeedableRng,

[src]

R: BlockRngCore + SeedableRng,

`impl<R> SeedableRng for BlockRng64<R> where`

R: BlockRngCore + SeedableRng,

[src]

R: BlockRngCore + SeedableRng,

`type Seed = <R as SeedableRng>::Seed`

`fn from_seed(seed: <BlockRng64<R> as SeedableRng>::Seed) -> BlockRng64<R>`

[src]

`fn from_rng<S>(rng: S) -> Result<BlockRng64<R>, Error> where`

S: RngCore,

[src]

S: RngCore,

## Implementors

`impl SeedableRng for ChaChaRng type Seed = <ChaChaCore as SeedableRng>::Seed;`

`impl SeedableRng for ChaChaCore type Seed = [u8; 32];`

`impl SeedableRng for Hc128Rng type Seed = <Hc128Core as SeedableRng>::Seed;`

`impl SeedableRng for Hc128Core type Seed = [u8; 32];`

`impl SeedableRng for IsaacRng type Seed = <IsaacCore as SeedableRng>::Seed;`

`impl SeedableRng for IsaacCore type Seed = [u8; 32];`

`impl SeedableRng for Isaac64Rng type Seed = <Isaac64Core as SeedableRng>::Seed;`

`impl SeedableRng for Isaac64Core type Seed = [u8; 32];`

`impl SeedableRng for XorShiftRng type Seed = [u8; 16];`

`impl SeedableRng for SmallRng type Seed = <XorShiftRng as SeedableRng>::Seed;`

`impl SeedableRng for StdRng type Seed = <Hc128Rng as SeedableRng>::Seed;`