Troschuetz.Random

Fully managed library providing various random number generators and distributions.

Summary

Latest release version: v4.2.0
Build status on Travis CI:
Build status on AppVeyor:
Wyam generated API documentation: https://pomma89.github.io/Troschuetz.Random/api/
NuGet package(s):
- Troschuetz.Random

How to build

Windows

Clone the project, go to the root and run PowerShell script build.ps1. In order for it to work, you need:

At least Windows 10 Fall Creators Update
At least Visual Studio 2017 Update 4
.NET Framework 4.7.1 Developer Pack
.NET Core 2.0 SDK

Linux

Clone the project, go to the root and run Bash script build.sh. In order for it to work, you need:

TODO, still need to make it building reliably.

Introduction

All the hard work behind this library was done by Stefan Troschütz, and for which I thank him very much. What I have done with his great project, was simply to refactor and improve his code, while offering a new Python-style random class.

Please visit the page of the original project in order to get an overview of the contents of this library. Unluckily, linked article also contains the only documentation available.

Tester

A simple, yet effective, WinForms application is available in order to test the Troschuetz.Random library. As for the rest of the code, that application was completely written by Stefan Troschütz and what I did was simply to adapt it to the new refactored code.

The tester is now distributed on NuGet, embedded inside the main Troschuetz.Random NuGet package. Just look for the "tester" folder, it contains everything needed to run and play with the tester.

Basic usage

See example below to understand how you can use the library:

using System;
using System.Linq;
using Troschuetz.Random.Distributions.Continuous;
using Troschuetz.Random.Generators;

namespace Troschuetz.Random.Examples
{
    /// <summary>
    ///   Some examples of what you can do with this library.
    /// </summary>
    static class UsageExamples
    {
        public static void Main()
        {
            // 1) Use TRandom to generate a few random numbers - via IGenerator methods.
            Console.WriteLine("TRandom in action, used as an IGenerator");
            var trandom = new TRandom();
            Console.WriteLine(trandom.Next() - trandom.Next(5) + trandom.Next(3, 5));
            Console.WriteLine(trandom.NextDouble() * trandom.NextDouble(5.5) * trandom.NextDouble(10.1, 21.9));
            Console.WriteLine(trandom.NextBoolean());

            Console.WriteLine();

            // 2) Use TRandom to generate a few random numbers - via extension methods.
            Console.WriteLine("TRandom in action, used as an IGenerator augmented with extension methods");
            Console.WriteLine(string.Join(", ", trandom.Integers().Take(10)));
            Console.WriteLine(string.Join(", ", trandom.Doubles().Take(10)));
            Console.WriteLine(string.Join(", ", trandom.Booleans().Take(10)));

            Console.WriteLine();

            // 3) Use TRandom to generate a few distributed numbers.
            Console.WriteLine("TRandom in action, used as to get distributed numbers");
            Console.WriteLine(trandom.Normal(1.0, 0.1));
            Console.WriteLine(string.Join(", ", trandom.NormalSamples(1.0, 0.1).Take(20)));
            Console.WriteLine(trandom.Poisson(5));
            Console.WriteLine(string.Join(", ", trandom.PoissonSamples(5).Take(20)));

            Console.WriteLine();

            // 4) There are many generators available - XorShift128 is the default.
            var alf = new ALFGenerator(TMath.Seed());
            var nr3 = new NR3Generator();
            var std = new StandardGenerator(127);

            // 5) You can also use distribution directly, even with custom generators.
            Console.WriteLine("Showcase of some distributions");
            Console.WriteLine("Static sample for Normal: " + NormalDistribution.Sample(alf, 1.0, 0.1));
            Console.WriteLine("New instance for Normal: " + new NormalDistribution(1.0, 0.1).NextDouble());

            Console.WriteLine();
        }
    }
}

Extensibility

After a request from a user, the library has been modified in order to allow it to be easily extended or modified.

Starting from version 4.0, these extensibility cases are supported:

Defining a custom generator by extending the AbstractGenerator class.
Defining a custom distribution, by extending the AbstractDistribution class and by implementing either IContinuousDistribution or IDiscreteDistribution.
Change the core definition of a standard distribution, by redefining the static Sample delegate, used to generate distributed numbers, and the static IsValidParam/AreValidParams delegate, used to validate parameters.

For your convenience, below you can find an example of how you can implement all features above (the code is also present in the Examples project):

using System;
using System.Linq;
using Troschuetz.Random.Distributions;
using Troschuetz.Random.Distributions.Continuous;
using Troschuetz.Random.Generators;

namespace Troschuetz.Random.Examples
{
    /// <summary>
    ///   Examples on how the library can be extended or modified.
    /// </summary>
    static class ExtensibilityExamples
    {
        public static void Main()
        {
            // 1) Use SuperSillyGenerator to generate a few numbers.
            Console.WriteLine("Super silly generator in action!");
            var ssg = new SuperSillyGenerator(21U);
            foreach (var x in ssg.Doubles().Take(5)) Console.WriteLine(x);

            Console.WriteLine();

            // 2) Use SuperSillyContinuousDistribution to generate a few numbers.
            Console.WriteLine("Super silly distribution in action!");
            var ssd = new SuperSillyContinuousDistribution(ssg);
            Console.WriteLine(ssd.NextDouble());
            Console.WriteLine(ssd.NextDouble());
            Console.WriteLine(ssd.NextDouble());

            Console.WriteLine();

            // 3) Use SuperSillyGenerator with a normal distribution.
            Console.WriteLine("Super silly generator with a normal distribution");
            var normal = new NormalDistribution(ssg, 0, 1);
            Console.WriteLine(normal.NextDouble());
            Console.WriteLine(normal.NextDouble());
            Console.WriteLine(normal.NextDouble());

            Console.WriteLine();

            // 4) Change the core logic of normal distribution with a... Silly one.
            Console.WriteLine("Super silly redefinition of a normal distribution");
            NormalDistribution.Sample = (generator, mu, sigma) =>
            {
                // Silly method!!!
                return generator.NextDouble() + mu + sigma + mu * sigma;
            };
            Console.WriteLine(normal.NextDouble());
            Console.WriteLine(normal.NextDouble());
            Console.WriteLine(normal.NextDouble());

            Console.WriteLine();

            // 5) Use the new logic, even through TRandom.
            Console.WriteLine("Super silly redefinition of a normal distribution - via TRandom");
            var trandom = TRandom.New();
            Console.WriteLine(trandom.Normal(5, 0.5));
            Console.WriteLine(trandom.Normal(5, 0.5));
            Console.WriteLine(trandom.Normal(5, 0.5));

            Console.WriteLine();

            // 6) Use the extension method you defined for SuperSillyContinuousDistribution.
            Console.WriteLine("Super silly distribution via TRandom");
            Console.WriteLine(trandom.SuperSilly());
            Console.WriteLine(trandom.SuperSilly());
            Console.WriteLine(trandom.SuperSilly());
        }
    }

    // Super silly generator, which is provided as an example on how one can build a new generator.
    // Of course, never use this generator in production... It is super silly, after all.
    class SuperSillyGenerator : AbstractGenerator
    {
        // The state of the generaror, which usually consists of one or more variables. The initial
        // state, maybe depending on the seed, should be set by overriding the Reset method.
        uint _state;

        // Just a simple constructor which passes the seed to the base constructor.
        public SuperSillyGenerator(uint seed) : base(seed)
        {
        }

        // Should return true if your generator can reset, false otherwise.
        public override bool CanReset => true;

        // Here you should handle the state of your generator. ALWAYS remember to call
        // base.Reset(seed), since it is necessary to correctly reset the AbstractGenerator.
        public override bool Reset(uint seed)
        {
            base.Reset(seed);
            _state = seed;
            return true;
        }

        // You must provide only three generation methods; from these methods, the AbstractGenerator
        // returns all other necessary objects.
        public override int NextInclusiveMaxValue() => (int) (++_state >> 1);
        public override double NextDouble() => ++_state * UIntToDoubleMultiplier;
        public override uint NextUInt() => ++_state;
    }

    // Super silly continuous distribution which is provided as an example on how one can build a
    // new distribution. Of course, never use this distribution in production... It is super silly,
    // after all.
    class SuperSillyContinuousDistribution : AbstractDistribution, IContinuousDistribution
    {
        // Just a simple constructor which passes the generator to the base constructor.
        public SuperSillyContinuousDistribution(IGenerator generator) : base(generator)
        {
        }

        public double Minimum => 0.0;

        public double Maximum => 1.0;

        public double Mean => 0.5;

        public double Median => 0.5;

        public double[] Mode
        {
            get { throw new NotSupportedException(); }
        }

        public double Variance => 1.0 / 12.0;

        // The generation method, in which you define the logic of your distribution.
        public double NextDouble() => Sample(Generator);

        // Optional: Define your logic in a delegate, so that you can reuse it in TRandom or let
        //           others change it, if necessary. All standard distributions do this.
        public static Func<IGenerator, double> Sample { get; set; } = generator => generator.NextDouble();
    }

    // An example of how to enrich the TRandom class with your custom distribution.
    static class TRandomExtensions
    {
        // Simply call the static Sample you defined above. In the Main function, you can see that
        // this method can be used as other methods defined in TRandom.
        public static double SuperSilly(this TRandom r) => SuperSillyContinuousDistribution.Sample(r.Generator);
    }
}

Benchmarks

All benchmarks are implemented with BenchmarkDotNet.

Generator comparison

BenchmarkDotNet=v0.10.9, OS=Windows 7 SP1 (6.1.7601)
Processor=Intel Xeon CPU E5-2640 0 2.50GHzIntel Xeon CPU E5-2640 0 2.50GHz, ProcessorCount=4
Frequency=14318180 Hz, Resolution=69.8413 ns, Timer=HPET
  [Host]    : .NET Framework 4.6.2 (CLR 4.0.30319.42000), 64bit RyuJIT-v4.7.2114.0
  RyuJitX64 : .NET Framework 4.6.2 (CLR 4.0.30319.42000), 64bit RyuJIT-v4.7.2114.0

Job=RyuJitX64  Jit=RyuJit  Platform=X64

Method	Generator	Mean	Error	StdDev	Median	Allocated
NextBytes64	ALF	204.40 ns	15.1778 ns	43.7913 ns	175.67 ns	0 B
NextBytes128	ALF	698.23 ns	24.8372 ns	70.0537 ns	725.99 ns	0 B
NextDouble	ALF	73.48 ns	8.8984 ns	26.2371 ns	73.96 ns	0 B
NextUInt	ALF	84.17 ns	8.5350 ns	25.1658 ns	93.41 ns	0 B
NextBoolean	ALF	83.22 ns	8.8154 ns	25.9925 ns	97.18 ns	0 B
NextBytes64	MT19937	390.19 ns	42.1964 ns	124.4171 ns	385.31 ns	1 B
NextBytes128	MT19937	572.99 ns	60.9795 ns	176.9126 ns	466.00 ns	2 B
NextDouble	MT19937	106.78 ns	9.9708 ns	29.3992 ns	116.91 ns	0 B
NextUInt	MT19937	89.92 ns	12.0589 ns	35.5558 ns	80.62 ns	0 B
NextBoolean	MT19937	123.84 ns	1.3634 ns	1.1385 ns	123.83 ns	0 B
NextBytes64	NR3	332.23 ns	30.9759 ns	91.3330 ns	367.56 ns	0 B
NextBytes128	NR3	516.73 ns	43.8408 ns	129.2655 ns	518.97 ns	0 B
NextDouble	NR3	47.71 ns	0.9843 ns	1.1335 ns	47.39 ns	0 B
NextUInt	NR3	46.58 ns	0.1341 ns	0.1254 ns	46.55 ns	0 B
NextBoolean	NR3	81.48 ns	9.3388 ns	27.5358 ns	88.94 ns	0 B
NextBytes64	NR3Q1	293.89 ns	28.4659 ns	83.9323 ns	341.38 ns	0 B
NextBytes128	NR3Q1	525.39 ns	48.5404 ns	143.1226 ns	597.57 ns	0 B
NextDouble	NR3Q1	80.49 ns	8.6479 ns	25.4984 ns	92.60 ns	0 B
NextUInt	NR3Q1	69.00 ns	9.0288 ns	26.6217 ns	67.20 ns	0 B
NextBoolean	NR3Q1	76.92 ns	9.0602 ns	26.7143 ns	80.57 ns	0 B
NextBytes64	NR3Q2	268.61 ns	30.4143 ns	89.6772 ns	287.60 ns	0 B
NextBytes128	NR3Q2	501.28 ns	42.7137 ns	125.9424 ns	531.24 ns	0 B
NextDouble	NR3Q2	88.23 ns	8.2374 ns	24.2883 ns	99.62 ns	0 B
NextUInt	NR3Q2	80.96 ns	8.7471 ns	25.7910 ns	90.41 ns	0 B
NextBoolean	NR3Q2	82.56 ns	8.9897 ns	26.5062 ns	86.25 ns	0 B
NextBytes64	Standard	2,024.02 ns	209.0748 ns	616.4617 ns	2,400.90 ns	0 B
NextBytes128	Standard	3,918.49 ns	366.4296 ns	1,080.4260 ns	4,520.70 ns	0 B
NextDouble	Standard	63.75 ns	0.1743 ns	0.1361 ns	63.72 ns	0 B
NextUInt	Standard	215.26 ns	20.9545 ns	61.7849 ns	249.28 ns	0 B
NextBoolean	Standard	94.98 ns	8.8891 ns	26.2098 ns	101.87 ns	0 B
NextBytes64	XorShift128	272.72 ns	30.7428 ns	90.6458 ns	296.95 ns	0 B
NextBytes128	XorShift128	484.33 ns	55.2191 ns	162.8147 ns	550.33 ns	0 B
NextDouble	XorShift128	101.53 ns	11.2477 ns	33.1640 ns	103.34 ns	0 B
NextUInt	XorShift128	61.39 ns	12.1388 ns	10.7607 ns	58.20 ns	0 B
NextBoolean	XorShift128	103.31 ns	10.6770 ns	31.4813 ns	102.86 ns	0 B

Continuous distribution comparison

BenchmarkDotNet=v0.10.9, OS=Windows 7 SP1 (6.1.7601)
Processor=Intel Xeon CPU E5-2640 0 2.50GHzIntel Xeon CPU E5-2640 0 2.50GHz, ProcessorCount=4
Frequency=14318180 Hz, Resolution=69.8413 ns, Timer=HPET
  [Host]    : .NET Framework 4.6.2 (CLR 4.0.30319.42000), 64bit RyuJIT-v4.7.2114.0
  RyuJitX64 : .NET Framework 4.6.2 (CLR 4.0.30319.42000), 64bit RyuJIT-v4.7.2114.0

Job=RyuJitX64  Jit=RyuJit  Platform=X64

Method	Distribution	Generator	Mean	Error	StdDev	Median
NextDouble	Beta	ALF	338.8 ns	6.8819 ns	17.1383 ns	334.1 ns
NextDouble	Beta	MT19937	375.0 ns	7.4529 ns	7.6535 ns	372.5 ns
NextDouble	Beta	NR3	385.9 ns	29.4146 ns	84.8678 ns	331.7 ns
NextDouble	Beta	NR3Q1	381.7 ns	31.7568 ns	92.6361 ns	320.7 ns
NextDouble	Beta	NR3Q2	324.3 ns	8.4574 ns	10.6959 ns	321.6 ns
NextDouble	Beta	Standard	676.8 ns	72.1428 ns	212.7146 ns	740.6 ns
NextDouble	Beta	XorShift128	525.4 ns	61.5321 ns	181.4288 ns	550.6 ns
NextDouble	BetaPrime	ALF	590.4 ns	63.1414 ns	186.1738 ns	611.7 ns
NextDouble	BetaPrime	MT19937	631.2 ns	69.2676 ns	204.2371 ns	654.7 ns
NextDouble	BetaPrime	NR3	574.2 ns	67.0142 ns	197.5929 ns	593.5 ns
NextDouble	BetaPrime	NR3Q1	529.6 ns	65.0692 ns	191.8580 ns	578.1 ns
NextDouble	BetaPrime	NR3Q2	568.9 ns	49.2187 ns	145.1224 ns	566.5 ns
NextDouble	BetaPrime	Standard	930.8 ns	14.6964 ns	15.0921 ns	934.3 ns
NextDouble	BetaPrime	XorShift128	619.9 ns	64.4471 ns	190.0238 ns	698.5 ns
NextDouble	Cauchy	ALF	284.1 ns	28.4789 ns	83.9707 ns	322.2 ns
NextDouble	Cauchy	MT19937	305.8 ns	31.4928 ns	92.8573 ns	340.4 ns
NextDouble	Cauchy	NR3	250.0 ns	25.2609 ns	74.4824 ns	240.8 ns
NextDouble	Cauchy	NR3Q1	281.4 ns	26.9234 ns	79.3843 ns	316.6 ns
NextDouble	Cauchy	NR3Q2	261.6 ns	23.9230 ns	70.5375 ns	260.7 ns
NextDouble	Cauchy	Standard	292.1 ns	31.0940 ns	91.6812 ns	321.7 ns
NextDouble	Cauchy	XorShift128	314.6 ns	30.0235 ns	88.5249 ns	359.6 ns
NextDouble	Chi	ALF	290.5 ns	24.4455 ns	72.0782 ns	291.2 ns
NextDouble	Chi	MT19937	229.4 ns	20.0623 ns	57.5624 ns	188.7 ns
NextDouble	Chi	NR3	315.2 ns	30.1669 ns	88.9478 ns	341.8 ns
NextDouble	Chi	NR3Q1	312.2 ns	30.9188 ns	91.1648 ns	353.1 ns
NextDouble	Chi	NR3Q2	370.4 ns	13.3744 ns	35.6990 ns	386.5 ns
NextDouble	Chi	Standard	371.1 ns	36.5695 ns	107.8259 ns	428.8 ns
NextDouble	Chi	XorShift128	336.5 ns	33.9692 ns	100.1589 ns	392.2 ns
NextDouble	ChiSquare	ALF	308.4 ns	34.0179 ns	100.3025 ns	343.7 ns
NextDouble	ChiSquare	MT19937	357.7 ns	37.1976 ns	109.6781 ns	408.7 ns
NextDouble	ChiSquare	NR3	320.5 ns	35.3093 ns	104.1104 ns	364.8 ns
NextDouble	ChiSquare	NR3Q1	320.2 ns	32.0234 ns	94.4218 ns	355.5 ns
NextDouble	ChiSquare	NR3Q2	314.0 ns	32.9729 ns	97.2214 ns	327.7 ns
NextDouble	ChiSquare	Standard	389.2 ns	38.0037 ns	112.0548 ns	447.8 ns
NextDouble	ChiSquare	XorShift128	321.3 ns	37.5629 ns	110.7552 ns	340.6 ns
NextDouble	ContinuousUniform	ALF	199.9 ns	22.7095 ns	66.9595 ns	231.3 ns
NextDouble	ContinuousUniform	MT19937	180.4 ns	24.7217 ns	72.8926 ns	125.0 ns
NextDouble	ContinuousUniform	NR3	251.1 ns	10.2896 ns	27.9937 ns	266.0 ns
NextDouble	ContinuousUniform	NR3Q1	254.6 ns	5.0959 ns	9.5713 ns	259.3 ns
NextDouble	ContinuousUniform	NR3Q2	194.1 ns	19.3062 ns	56.9249 ns	201.2 ns
NextDouble	ContinuousUniform	Standard	238.8 ns	24.1279 ns	71.1418 ns	271.1 ns
NextDouble	ContinuousUniform	XorShift128	191.6 ns	22.9668 ns	67.7182 ns	187.2 ns
NextDouble	Exponential	ALF	284.6 ns	5.6958 ns	6.3309 ns	286.7 ns
NextDouble	Exponential	MT19937	307.3 ns	11.7584 ns	32.1885 ns	323.7 ns
NextDouble	Exponential	NR3	206.6 ns	22.0528 ns	65.0232 ns	195.7 ns
NextDouble	Exponential	NR3Q1	228.9 ns	24.6797 ns	72.7686 ns	263.5 ns
NextDouble	Exponential	NR3Q2	214.4 ns	24.5962 ns	72.5225 ns	241.8 ns
NextDouble	Exponential	Standard	229.5 ns	21.5897 ns	63.6576 ns	214.6 ns
NextDouble	Exponential	XorShift128	223.2 ns	27.7926 ns	81.9471 ns	226.4 ns
NextDouble	Normal	ALF	278.4 ns	29.4686 ns	86.8889 ns	321.5 ns
NextDouble	Normal	MT19937	273.2 ns	31.4820 ns	92.8255 ns	272.5 ns
NextDouble	Normal	NR3	264.9 ns	30.8614 ns	90.9954 ns	284.1 ns
NextDouble	Normal	NR3Q1	244.4 ns	24.4235 ns	72.0134 ns	247.0 ns
NextDouble	Normal	NR3Q2	243.7 ns	23.5501 ns	69.4379 ns	243.1 ns
NextDouble	Normal	Standard	187.3 ns	0.3546 ns	0.2769 ns	187.3 ns
NextDouble	Normal	XorShift128	169.1 ns	4.9066 ns	6.2053 ns	167.4 ns

Discrete distribution comparison

BenchmarkDotNet=v0.10.9, OS=Windows 7 SP1 (6.1.7601)
Processor=Intel Xeon CPU E5-2640 0 2.50GHzIntel Xeon CPU E5-2640 0 2.50GHz, ProcessorCount=4
Frequency=14318180 Hz, Resolution=69.8413 ns, Timer=HPET
  [Host]    : .NET Framework 4.6.2 (CLR 4.0.30319.42000), 64bit RyuJIT-v4.7.2114.0
  RyuJitX64 : .NET Framework 4.6.2 (CLR 4.0.30319.42000), 64bit RyuJIT-v4.7.2114.0

Job=RyuJitX64  Jit=RyuJit  Platform=X64

Method	Distribution	Generator	Mean	Error	StdDev	Median	Allocated
NextDouble	Bernoulli	ALF	160.4 ns	14.4213 ns	42.5215 ns	159.9 ns	0 B
Next	Bernoulli	ALF	197.2 ns	19.8672 ns	58.5790 ns	218.1 ns	0 B
NextDouble	Bernoulli	MT19937	122.9 ns	7.8789 ns	17.2943 ns	117.9 ns	0 B
Next	Bernoulli	MT19937	123.5 ns	4.2766 ns	4.9250 ns	122.2 ns	0 B
NextDouble	Bernoulli	NR3	111.6 ns	4.2093 ns	3.7315 ns	110.6 ns	0 B
Next	Bernoulli	NR3	201.8 ns	21.8658 ns	64.4719 ns	235.6 ns	0 B
NextDouble	Bernoulli	NR3Q1	180.2 ns	21.2046 ns	62.5223 ns	198.5 ns	0 B
Next	Bernoulli	NR3Q1	201.1 ns	18.6531 ns	54.9990 ns	229.9 ns	0 B
NextDouble	Bernoulli	NR3Q2	245.0 ns	8.0769 ns	21.9739 ns	257.2 ns	0 B
Next	Bernoulli	NR3Q2	216.1 ns	19.1118 ns	56.3514 ns	246.2 ns	0 B
NextDouble	Bernoulli	Standard	200.3 ns	24.5942 ns	72.5165 ns	203.9 ns	0 B
Next	Bernoulli	Standard	231.8 ns	21.4910 ns	63.3668 ns	261.8 ns	0 B
NextDouble	Bernoulli	XorShift128	228.5 ns	22.2808 ns	65.6955 ns	248.7 ns	0 B
Next	Bernoulli	XorShift128	212.7 ns	20.9942 ns	61.9017 ns	208.3 ns	0 B
NextDouble	Binomial	ALF	136.5 ns	13.5852 ns	40.0562 ns	107.3 ns	0 B
Next	Binomial	ALF	204.5 ns	17.6937 ns	52.1703 ns	231.6 ns	0 B
NextDouble	Binomial	MT19937	196.5 ns	20.8854 ns	61.5810 ns	196.3 ns	0 B
Next	Binomial	MT19937	209.9 ns	22.7067 ns	66.9513 ns	242.0 ns	0 B
NextDouble	Binomial	NR3	210.2 ns	19.4362 ns	57.3080 ns	239.8 ns	0 B
Next	Binomial	NR3	203.3 ns	20.3067 ns	59.8747 ns	237.6 ns	0 B
NextDouble	Binomial	NR3Q1	251.8 ns	0.9306 ns	0.6729 ns	251.7 ns	0 B
Next	Binomial	NR3Q1	203.8 ns	20.4505 ns	60.2987 ns	236.1 ns	0 B
NextDouble	Binomial	NR3Q2	192.4 ns	21.5017 ns	63.3983 ns	228.4 ns	0 B
Next	Binomial	NR3Q2	165.7 ns	15.5334 ns	45.8007 ns	166.7 ns	0 B
NextDouble	Binomial	Standard	163.8 ns	22.7104 ns	66.9623 ns	113.2 ns	0 B
Next	Binomial	Standard	202.4 ns	22.6595 ns	66.8120 ns	228.1 ns	0 B
NextDouble	Binomial	XorShift128	207.7 ns	25.3699 ns	74.8038 ns	231.4 ns	0 B
Next	Binomial	XorShift128	216.8 ns	20.1394 ns	59.3815 ns	233.6 ns	0 B
NextDouble	Categorical	ALF	270.5 ns	21.5656 ns	63.5867 ns	311.0 ns	0 B
Next	Categorical	ALF	221.6 ns	28.3732 ns	83.6589 ns	246.6 ns	0 B
NextDouble	Categorical	MT19937	258.9 ns	27.7546 ns	81.8351 ns	292.4 ns	0 B
Next	Categorical	MT19937	273.2 ns	25.3398 ns	74.7149 ns	311.0 ns	0 B
NextDouble	Categorical	NR3	210.9 ns	27.4160 ns	80.8366 ns	206.7 ns	0 B
Next	Categorical	NR3	221.3 ns	21.3502 ns	62.9516 ns	222.7 ns	0 B
NextDouble	Categorical	NR3Q1	225.5 ns	25.6885 ns	75.7432 ns	257.5 ns	0 B
Next	Categorical	NR3Q1	219.8 ns	26.3636 ns	77.7335 ns	233.2 ns	0 B
NextDouble	Categorical	NR3Q2	221.3 ns	21.2556 ns	62.6725 ns	224.7 ns	0 B
Next	Categorical	NR3Q2	217.9 ns	25.3020 ns	74.6035 ns	214.6 ns	0 B
NextDouble	Categorical	Standard	258.7 ns	28.0689 ns	82.7619 ns	302.6 ns	0 B
Next	Categorical	Standard	223.5 ns	28.6035 ns	84.3382 ns	224.6 ns	0 B
NextDouble	Categorical	XorShift128	251.4 ns	26.8065 ns	79.0395 ns	273.6 ns	0 B
Next	Categorical	XorShift128	257.2 ns	27.1336 ns	80.0041 ns	280.5 ns	0 B
NextDouble	DiscreteUniform	ALF	150.4 ns	15.2359 ns	44.9233 ns	141.2 ns	0 B
Next	DiscreteUniform	ALF	215.8 ns	20.1608 ns	59.4445 ns	247.6 ns	0 B
NextDouble	DiscreteUniform	MT19937	215.5 ns	25.1271 ns	74.0877 ns	243.9 ns	0 B
Next	DiscreteUniform	MT19937	228.2 ns	22.6271 ns	66.7164 ns	256.9 ns	0 B
NextDouble	DiscreteUniform	NR3	201.1 ns	19.5358 ns	57.6019 ns	209.7 ns	0 B
Next	DiscreteUniform	NR3	206.3 ns	22.2678 ns	65.6570 ns	241.6 ns	0 B
NextDouble	DiscreteUniform	NR3Q1	199.5 ns	22.1306 ns	65.2527 ns	226.3 ns	0 B
Next	DiscreteUniform	NR3Q1	188.5 ns	20.7400 ns	61.1524 ns	184.1 ns	0 B
NextDouble	DiscreteUniform	NR3Q2	174.7 ns	16.0187 ns	47.2315 ns	174.7 ns	0 B
Next	DiscreteUniform	NR3Q2	176.7 ns	23.4391 ns	69.1108 ns	151.1 ns	0 B
NextDouble	DiscreteUniform	Standard	227.7 ns	23.7944 ns	70.1582 ns	262.2 ns	0 B
Next	DiscreteUniform	Standard	209.3 ns	26.5376 ns	78.2467 ns	198.8 ns	0 B
NextDouble	DiscreteUniform	XorShift128	216.1 ns	25.4030 ns	74.9014 ns	243.1 ns	0 B
Next	DiscreteUniform	XorShift128	164.7 ns	18.3713 ns	54.1682 ns	126.4 ns	0 B
NextDouble	Geometric	ALF	276.2 ns	7.8966 ns	21.6168 ns	287.8 ns	0 B
Next	Geometric	ALF	232.5 ns	23.4024 ns	69.0024 ns	271.3 ns	0 B
NextDouble	Geometric	MT19937	262.1 ns	27.7712 ns	81.8840 ns	310.9 ns	0 B
Next	Geometric	MT19937	266.8 ns	26.1382 ns	77.0690 ns	301.6 ns	0 B
NextDouble	Geometric	NR3	240.3 ns	23.0080 ns	67.8395 ns	276.8 ns	0 B
Next	Geometric	NR3	212.5 ns	25.0057 ns	73.7298 ns	233.6 ns	0 B
NextDouble	Geometric	NR3Q1	209.8 ns	20.9796 ns	61.8590 ns	216.2 ns	0 B
Next	Geometric	NR3Q1	208.8 ns	22.0906 ns	65.1345 ns	218.9 ns	0 B
NextDouble	Geometric	NR3Q2	203.2 ns	20.7195 ns	61.0919 ns	202.0 ns	0 B
Next	Geometric	NR3Q2	120.4 ns	3.7409 ns	8.0527 ns	117.9 ns	0 B
NextDouble	Geometric	Standard	138.1 ns	0.3606 ns	0.3011 ns	138.1 ns	0 B
Next	Geometric	Standard	140.4 ns	6.9079 ns	17.8316 ns	134.7 ns	0 B
NextDouble	Geometric	XorShift128	133.2 ns	10.3824 ns	10.6620 ns	130.3 ns	0 B
Next	Geometric	XorShift128	251.7 ns	26.4673 ns	78.0394 ns	295.4 ns	0 B
NextDouble	Poisson	ALF	276.0 ns	26.6990 ns	78.7225 ns	320.4 ns	0 B
Next	Poisson	ALF	141.8 ns	1.4151 ns	1.1817 ns	141.4 ns	0 B
NextDouble	Poisson	MT19937	160.6 ns	0.2389 ns	0.2235 ns	160.6 ns	0 B
Next	Poisson	MT19937	182.0 ns	14.8377 ns	42.5722 ns	157.9 ns	0 B
NextDouble	Poisson	NR3	189.9 ns	24.5821 ns	72.4807 ns	142.0 ns	0 B
Next	Poisson	NR3	139.8 ns	1.3770 ns	1.0751 ns	139.5 ns	0 B
NextDouble	Poisson	NR3Q1	178.7 ns	71.5763 ns	73.5036 ns	139.5 ns	0 B
Next	Poisson	NR3Q1	140.7 ns	2.9420 ns	3.9275 ns	139.5 ns	0 B
NextDouble	Poisson	NR3Q2	274.8 ns	27.1575 ns	80.0746 ns	302.3 ns	0 B
Next	Poisson	NR3Q2	265.4 ns	26.8771 ns	79.2477 ns	312.5 ns	0 B
NextDouble	Poisson	Standard	286.9 ns	32.0805 ns	94.5900 ns	323.0 ns	0 B
Next	Poisson	Standard	288.9 ns	30.4191 ns	89.6915 ns	316.8 ns	0 B
NextDouble	Poisson	XorShift128	273.9 ns	30.1290 ns	88.8361 ns	298.3 ns	0 B
Next	Poisson	XorShift128	277.5 ns	29.6678 ns	87.4762 ns	312.3 ns	0 B

About this repository and its maintainer

Everything done on this repository is freely offered on the terms of the project license. You are free to do everything you want with the code and its related files, as long as you respect the license and use common sense while doing it :-)

I maintain this project during my spare time, so I can offer limited assistance and I can offer no kind of warranty.

However, if this project helps you, then you might offer me an hot cup of coffee:

Name		Name	Last commit message	Last commit date
Latest commit History 258 Commits
docs		docs
logo		logo
pages		pages
src/Troschuetz.Random		src/Troschuetz.Random
test		test
tools		tools
.gitattributes		.gitattributes
.gitignore		.gitignore
.travis.yml		.travis.yml
CHANGELOG.md		CHANGELOG.md
LICENSE.md		LICENSE.md
NuGet.Config		NuGet.Config
README.md		README.md
RandomTester_src.zip		RandomTester_src.zip
Random_src.zip		Random_src.zip
Troschuetz.Random.sln		Troschuetz.Random.sln
appveyor.yml		appveyor.yml
build.cake		build.cake
build.ps1		build.ps1
build.sh		build.sh
config.wyam		config.wyam
config.wyam.dll		config.wyam.dll
config.wyam.hash		config.wyam.hash
packages.xml		packages.xml
pomma89.snk		pomma89.snk

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