Struct rand_pcg::Mcg128Xsl64

[+] Show declaration

pub struct Mcg128Xsl64 { /* fields omitted */ }

[−] Expand description

A PCG random number generator (XSL 128/64 (MCG) variant).

Permuted Congruential Generator with 128-bit state, internal Multiplicative Congruential Generator, and 64-bit output via "xorshift low (bits), random rotation" output function.

This is a 128-bit MCG with the PCG-XSL-RR output function. Note that compared to the standard pcg64 (128-bit LCG with PCG-XSL-RR output function), this RNG is faster, also has a long cycle, and still has good performance on statistical tests.

Note: this RNG is only available using Rust 1.26 or later.

Methods

impl Mcg128Xsl64

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pub fn new(state: u128) -> Self

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Construct an instance compatible with PCG seed.

Note that PCG specifies a default value for the parameter:

• state = 0xcafef00dd15ea5e5

Trait Implementations

impl Debug for Mcg128Xsl64

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

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Formats the value using the given formatter.

impl Clone for Mcg128Xsl64

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fn clone(&self) -> Mcg128Xsl64

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Returns a copy of the value.

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

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Performs copy-assignment from source.

impl SeedableRng for Mcg128Xsl64

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We use a single 126-bit seed to initialise the state and select a stream. Two seed bits (lowest order of last byte) are 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

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Create a new PRNG using the given seed.

fn seed_from_u64(state: u64) -> Self

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Create a new PRNG using a u64 seed.

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

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Create a new PRNG seeded from another Rng.

impl RngCore for Mcg128Xsl64

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fn next_u32(&mut self) -> u32

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Return the next random u32.

fn next_u64(&mut self) -> u64

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Return the next random u64.

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

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Fill dest with random data.

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

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Fill dest entirely with random data.