Random Number Generators | Extreme Optimization Numerical Libraries for .NET Professional |

So-called random number generators (RNG's) produce sequences of numbers that,
for the purposes of the user, appear to be random.
The **Extreme Optimization Numerical Libraries for .NET**
contains a number of different random number generators,
which vary in speed and quality.

The .NET framework already contains a random number generator. It is based on
an algorithm from Donald Knuth's *The Art of Computer Programming*.
For several reasons, this generator is not ideal. It scores badly on some
tests of randomness and has a serious bias in the least significant bit.
Nevertheless, the System

The ExtendedRandom
class extends System**Extreme Optimization Numerical Libraries for .NET**.

The Restart method allows you to reset the random number generator to its original state. After restarting, the generator always produces the same sequence of random numbers. It is also possible to pass a seed to the Restart(Int32) method. In this case, the generator is initialized with the specified seed.

All random number generators can be serialized. This means that their current state can be saved. When their state is restored at a later time, the random sequence can be continued from the point where it was saved.

In addition, several extension methods are provided generating sequences of random numbers, and for generating random numbers from any statistical distribution. These extension methods also work on the standard .NET Random class. These are defined in the RandomExtensions class.

One group of methods is used to obtain the next sample from a distribution. Next method, which returns a random sample from a discrete distribution, and the NextDouble method, which returns a random sample from a continuous distribution. These methods take the probability distribution as an argument.

The RandomExtensions class defines a Fill method with several overloads that fill existing arrays and vectors with a sequence of random numbers. Overloads of this method correspond to overloads of the Next and NextDouble methods, with an extra argument that specifies the object that is to receive the random numbers.

Finally, the AsParallel(Random) method wraps the random number generator in a thread-safe wrapper so it can be safely called from multiple threads.

The RANLUX suite of random number generators was developed by M. Luescher in 1994.
It generates 24 bit pseudo-random numbers. To reduce correlation
between successive values, a number of generated random numbers is
skipped occasionally. The period of is roughly 10^{171}.

The RANLUX suite is a prime example of the trade-off between quality and speed. Depending on the luxury level, the random numbers are from pretty good to perfect. The highest luxury level (3) produces 24 bit numbers which are proven to be totally random. This randomness comes at a cost: the best generator is about 3 times slower than the least good.

The RANLUX generators are implemented by the RanLux class. The luxury level is defined by the RanLuxLuxuryLevel enumeration. It can take on the following values:

Value | Description |
---|---|

Default | The fastest algorithm with some correlation between random values. |

Better | A medium-speed algorithm with reduced correlation between random values. |

Best | A slower algorithm without any correlation between random values. |

The RanLux class has four constructors. The first constructor has no arguments. It uses the default (fastest) luxury level, and uses a time-based seed. The second constructor has one argument: a RanLuxLuxuryLevel that specifies the luxury level. The third constructor takes one integer argument that is the initial seed value. The fourth constructor takes an integer seed and a RanLuxLuxuryLevel value. The following code snippet illustrates these constructors:

var ranLux1 = new RanLux(); var ranLux2 = new RanLux(99); var ranLux3 = new RanLux(RanLuxLuxuryLevel.Better); var ranLux4 = new RanLux(99, RanLuxLuxuryLevel.Best);

A Generalized Feedback Shift Register (GFSR) pseudo-random number generator
combines 4 values from a long sequence of registers to produce
each new 32 bit random number. Our implementation uses the values
proposed by the generator's developer Robert Ziff. It has a very long period
of about 10^{2917}.

Initialization time for this type of generator is relatively long. Generation of actual random numbers is very fast.

The generalized feedback shift register generator is implemented by the GfsrGenerator class. It has three constructors. The first constructor has no arguments. The second constructor takes one integer seed value. The third constructor takes an array of integers containing the seed values. The maximum length of this array is 16383.

var gfsr1 = new GfsrGenerator(); var gfsr2 = new GfsrGenerator(99); var gfsr3 = new GfsrGenerator(new int[] { 99, 17, int.MaxValue });

The Mersenne Twister is a random number generator
with an extremely long period - 2^{19937}, or roughly
1 followed by 6,000 zeros. It is relatively fast, and has good randomness properties.
It is a variation on a generalized feedback shift register generator.
It is of excellent quality and is comparable to other generators in speed.

The Mersenne Twister is implemented by the MersenneTwister class. It has three constructors. The first constructor has no arguments. The second constructor takes one integer seed value. The third constructor takes an array of integers containing the seed values. The maximum length of this array is 623.

var mersenne1 = new MersenneTwister(); var mersenne2 = new MersenneTwister(99); var mersenne3 = new MersenneTwister(new int[] { 99, 17, int.MaxValue });

No random number generator is perfect for every application. In fact, with the increase in computing power in recent decades have come an increasing number of failures of random number generators that were thought to be well-behaved. The application itself has become the testing ground for the quality of pseudo-random numbers.

We recommend testing your application with at least two different random number generators. If the results are significantly different, then at least one of the generators is not suitable. If the results are similar, you may wish to choose the faster of the two.

To help you make this decision, the table below lists the number of integer and double random numbers per second produced by each of the available pseudo-random number generators. The test machine was a 3GHz Pentium IV, and the unit is millions of random numbers per second.

Generator | Integers/s | Doubles/s |
---|---|---|

System.Random | 26.9 | 44.5 |

MersenneTwister | 36.7 | 13.0 |

RanLux (default) | 7.6 | 8.0 |

RanLux (better) | 5.0 | 5.2 |

RanLux (best) | 2.8 | 2.9 |

GsfrGenerator | 49.5 | 16.3 |

Donald E. Knuth, The Art of Computer Programming: Seminumerical Algorithms (Vol 2, 3rd Ed, 1997), Addison-Wesley.

M. Lscher, "A portable high-quality random number generator for lattice field theory calculations", Computer Physics Communications , 79 (1994) 100-110.

Makoto Matsumoto and Takuji Nishimura, "Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator". ACM Transactions on Modeling and Computer Simulation, Vol. 8, No. 1 (Jan. 1998), Pages 3-30

Robert M. Ziff, "Four-tap shift-register-sequence random-number generators", Computers in Physics, 12(4), Jul/Aug 1998, pp 385-392.

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