Pcg64 Class

public sealed class Pcg64 : RandomSource<ScalarGenerator64<Pcg64Engine>>, 
	IRandomSourceFactory<Pcg64>, IAdvanceable, IJumpable

Public NotInheritable Class Pcg64
	Inherits RandomSource(Of ScalarGenerator64(Of Pcg64Engine))
	Implements IRandomSourceFactory(Of Pcg64), IAdvanceable, 
	IJumpable

public ref class Pcg64 sealed : public RandomSource<ScalarGenerator64<Pcg64Engine>>, 
	IRandomSourceFactory<Pcg64^>, IAdvanceable, IJumpable

[<SealedAttribute>]
type Pcg64 = 
    class
        inherit RandomSource<ScalarGenerator64<Pcg64Engine>>
        interface IRandomSourceFactory<Pcg64>
        interface IAdvanceable
        interface IJumpable
    end

Inheritance

Object  →  RandomSource<ScalarGenerator64<Pcg64Engine>>  →  Pcg64

Implements

IAdvanceable, IJumpable, IRandomSourceFactory<Pcg64>

Use the Pcg64 class to represent a pseudo-random number generator that uses the PCG64 XSL-RR (Xorshift-Low, Random-Rotate) algorithm by Melissa O'Neill.

This generator features a 128-bit state with a period of 2¹²⁸. It passes the strictest statistical tests including BigCrush and PractRand, and provides excellent statistical properties and performance.

Algorithm details:

• LCG: 128-bit state
• Multiplier: 0x2360ED051FC65DA44385DF649FCCF645 (full 128-bit multiplier)
• Output function: XSL-RR 128/64 (Xorshift-Low, Random-Rotate)
• Output ordering: output-then-advance (outputs from state before advancing)

Use case: This is the reference PCG64 implementation matching the canonical specification with full 128-bit multiplication. It is compatible with NumPy 1.17+ PCG64 (which also uses the full multiplier) and serves as the academic/reference implementation.

Compatibility note: This generator will produce different sequences than Pcg64Dxsm even with the same seed, due to the different multiplier and/or output function.

PCG generators are notable for their mathematical soundness, predictability, and ease of implementing multiple independent streams.

Reference: Melissa E. O'Neill. "PCG: A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation." Harvey Mudd College, 2014.

See also: https://www.pcg-random.org/ and https://numpy.org/doc/stable/reference/random/bit_generators/pcg64.html

Reference