Struct rand_pcg::Lcg64Xsh32

pub struct Lcg64Xsh32 { /* fields omitted */ }

A PCG random number generator (XSH RR 64/32 (LCG) variant).

Permuted Congruential Generator with 64-bit state, internal Linear Congruential Generator, and 32-bit output via "xorshift high (bits), random rotation" output function.

This is a 64-bit LCG with explicitly chosen stream with the PCG-XSH-RR output function. This combination is the standard pcg32.

Despite the name, this implementation uses 16 bytes (128 bit) space comprising 64 bits of state and 64 bits stream selector. These are both set by SeedableRng, using a 128-bit seed.

Methods

impl Lcg64Xsh32

pub fn new(state: u64, stream: u64) -> Self

Construct an instance compatible with PCG seed and stream.

Note that PCG specifies default values for both parameters:

• state = 0xcafef00dd15ea5e5
• stream = 721347520444481703

Trait Implementations

impl Debug for Lcg64Xsh32

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Clone for Lcg64Xsh32

fn clone(&self) -> Lcg64Xsh32

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl SeedableRng for Lcg64Xsh32

We use a single 127-bit seed to initialise the state and select a stream. One seed bit (lowest bit of seed[8]) is ignored.

type Seed = [u8; 16]

Seed type, which is restricted to types mutably-dereferencable as u8 arrays (we recommend [u8; N] for some N).

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

Create a new PRNG using the given seed.

fn seed_from_u64(state: u64) -> Self

Create a new PRNG using a u64 seed.

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

Create a new PRNG seeded from another Rng.

impl RngCore for Lcg64Xsh32

fn next_u32(&mut self) -> u32

Return the next random u32.

fn next_u64(&mut self) -> u64

Return the next random u64.

fn fill_bytes(&mut self, dest: &mut [u8])

Fill dest with random data.

fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error>

Fill dest entirely with random data.