//End of ui.cs file Contents
//-------------------------------------------------------------------------
//Begin of Random.cs file contents
/// <summary>
/// Initializes the random-number generator with a specific seed.
/// </summary>
public void Initialize(uint seed)
{
RandomNumberGenerator = new MT19937Generator(seed);
betaDist = new BetaDistribution(RandomNumberGenerator);
betaPrimeDist = new BetaPrimeDistribution(RandomNumberGenerator);
cauchyDist = new CauchyDistribution(RandomNumberGenerator);
chiDist = new ChiDistribution(RandomNumberGenerator);
chiSquareDist = new ChiSquareDistribution(RandomNumberGenerator);
continuousUniformDist = new ContinuousUniformDistribution(RandomNumberGenerator);
erlangDist = new ErlangDistribution(RandomNumberGenerator);
exponentialDist = new ExponentialDistribution(RandomNumberGenerator);
fisherSnedecorDist = new FisherSnedecorDistribution(RandomNumberGenerator);
fisherTippettDist = new FisherTippettDistribution(RandomNumberGenerator);
gammaDist = new GammaDistribution(RandomNumberGenerator);
laplaceDist = new LaplaceDistribution(RandomNumberGenerator);
lognormalDist = new LognormalDistribution(RandomNumberGenerator);
normalDist = new NormalDistribution(RandomNumberGenerator);
paretoDist = new ParetoDistribution(RandomNumberGenerator);
powerDist = new PowerDistribution(RandomNumberGenerator);
rayleighDist = new RayleighDistribution(RandomNumberGenerator);
studentsTDist = new StudentsTDistribution(RandomNumberGenerator);
triangularDist = new TriangularDistribution(RandomNumberGenerator);
weibullDist = new WeibullDistribution(RandomNumberGenerator);
poissonDist = new PoissonDistribution(RandomNumberGenerator);
// generator.randomGenerator = new MT19937Generator(seed);
}
public void ConstructorTest()
{
var pareto = new ParetoDistribution(scale: 0.42, shape: 3);
double mean = pareto.Mean; // 0.63
double median = pareto.Median; // 0.52916684095584676
double var = pareto.Variance; // 0.13229999999999997
double cdf = pareto.DistributionFunction(x: 1.4); // 0.973
double pdf = pareto.ProbabilityDensityFunction(x: 1.4); // 0.057857142857142857
double lpdf = pareto.LogProbabilityDensityFunction(x: 1.4); // -2.8497783609309111
double ccdf = pareto.ComplementaryDistributionFunction(x: 1.4); // 0.027000000000000024
double icdf = pareto.InverseDistributionFunction(p: cdf); // 1.4000000446580794
double hf = pareto.HazardFunction(x: 1.4); // 2.142857142857141
double chf = pareto.CumulativeHazardFunction(x: 1.4); // 3.6119184129778072
string str = pareto.ToString(CultureInfo.InvariantCulture); // Pareto(x; xm = 0.42, α = 3)
Assert.AreEqual(0.63, mean);
Assert.AreEqual(0.52916684095584676, median);
Assert.AreEqual(0.13229999999999997, var);
Assert.AreEqual(3.6119184129778072, chf);
Assert.AreEqual(0.973, cdf);
Assert.AreEqual(0.057857142857142857, pdf);
Assert.AreEqual(-2.8497783609309111, lpdf);
Assert.AreEqual(2.142857142857141, hf);
Assert.AreEqual(0.027000000000000024, ccdf);
Assert.AreEqual(1.40, icdf, 1e-7);
Assert.AreEqual("Pareto(x; xm = 0.42, α = 3)", str);
}
public void MedianTest()
{
var target = new ParetoDistribution(scale: 7.12, shape: 2);
double median = target.Median;
Assert.AreEqual(10.069200564096438, median, 1e-10);
Assert.AreEqual(median, target.InverseDistributionFunction(0.5), 1e-6);
}
public void ConstructorTest()
{
var pareto = new ParetoDistribution(scale: 0.42, shape: 3);
double mean = pareto.Mean; // 0.63
double median = pareto.Median; // 0.52916684095584676
double var = pareto.Variance; // 0.13229999999999997
double mode = pareto.Mode; // 0.42
double cdf = pareto.DistributionFunction(x: 1.4); // 0.973
double pdf = pareto.ProbabilityDensityFunction(x: 1.4); // 0.057857142857142857
double lpdf = pareto.LogProbabilityDensityFunction(x: 1.4); // -2.8497783609309111
double ccdf = pareto.ComplementaryDistributionFunction(x: 1.4); // 0.027000000000000024
double icdf = pareto.InverseDistributionFunction(p: cdf); // 1.4000000446580794
double hf = pareto.HazardFunction(x: 1.4); // 2.142857142857141
double chf = pareto.CumulativeHazardFunction(x: 1.4); // 3.6119184129778072
string str = pareto.ToString(CultureInfo.InvariantCulture); // Pareto(x; xm = 0.42, α = 3)
Assert.AreEqual(0.63, mean);
Assert.AreEqual(0.52916684095584676, median);
Assert.AreEqual(0.13229999999999997, var);
Assert.AreEqual(0.42, mode, 1e-10);
Assert.AreEqual(3.6119184129778072, chf);
Assert.AreEqual(0.973, cdf);
Assert.AreEqual(0.057857142857142857, pdf);
Assert.AreEqual(-2.8497783609309111, lpdf);
Assert.AreEqual(2.142857142857141, hf);
Assert.AreEqual(0.027000000000000024, ccdf);
Assert.AreEqual(1.40, icdf, 1e-7);
Assert.AreEqual("Pareto(x; xm = 0.42, α = 3)", str);
var range1 = pareto.GetRange(0.95);
var range2 = pareto.GetRange(0.99);
var range3 = pareto.GetRange(0.01);
Assert.AreEqual(0.42724297039643383, range1.Min, 1e-8);
Assert.AreEqual(1.1400554029735852, range1.Max, 1e-8);
Assert.AreEqual(0.42140940651872005, range2.Min, 1e-8);
Assert.AreEqual(1.9494675279346789, range2.Max, 1e-8);
Assert.AreEqual(0.42140940651872005, range3.Min, 1e-8);
Assert.AreEqual(1.9494675279346789, range3.Max, 1e-8);
Assert.AreEqual(0.42, pareto.Support.Min);
Assert.AreEqual(double.PositiveInfinity, pareto.Support.Max);
Assert.AreEqual(pareto.InverseDistributionFunction(0), pareto.Support.Min);
Assert.AreEqual(pareto.InverseDistributionFunction(1), pareto.Support.Max);
}
public void FitTest()
{
var source = new ParetoDistribution(scale: 7.12, shape: 2);
var sample = new double[10000];
var step = 1.0 / sample.Length;
for (var i = 0; i < sample.Length; i++)
{
sample[i] = source.InverseDistributionFunction(i * step);
}
var target = new ParetoDistribution();
target.Fit(sample);
Assert.AreEqual(7.12, target.Scale, 1e-6);
Assert.AreEqual(2.0, target.Alpha, 1e-2);
}
public void ParetoDistributionConstructorTest()
{
double expected, actual;
{
ParetoDistribution target = new ParetoDistribution(3.1, 4.42);
actual = target.ProbabilityDensityFunction(-1);
expected = 0.0;
Assert.AreEqual(expected, actual, 1e-7);
Assert.IsFalse(Double.IsNaN(actual));
actual = target.ProbabilityDensityFunction(0);
expected = 0.0;
Assert.AreEqual(expected, actual, 1e-7);
Assert.IsFalse(Double.IsNaN(actual));
actual = target.ProbabilityDensityFunction(3.09);
expected = 0.0;
Assert.AreEqual(expected, actual, 1e-7);
Assert.IsFalse(Double.IsNaN(actual));
actual = target.ProbabilityDensityFunction(3.1);
expected = 1.4258064;
Assert.AreEqual(expected, actual, 1e-7);
Assert.IsFalse(Double.IsNaN(actual));
actual = target.ProbabilityDensityFunction(3.2);
expected = 1.20040576;
Assert.AreEqual(expected, actual, 1e-7);
Assert.IsFalse(Double.IsNaN(actual));
actual = target.ProbabilityDensityFunction(5.8);
expected = 0.0478037;
Assert.AreEqual(expected, actual, 1e-7);
Assert.IsFalse(Double.IsNaN(actual));
actual = target.ProbabilityDensityFunction(10);
expected = 0.00249598;
Assert.AreEqual(expected, actual, 1e-7);
Assert.IsFalse(Double.IsNaN(actual));
}
}
public void TestParetoDistribution()
{
double[][] para =
{
new double[] { 1.5, 1.75, 1.768010430487225851453548, 0.742359873769694378113828, 8.30877447091612868489998, 0.0105310356306195679153012 }
};
for (int i = 0; i < para.Length; i++)
{
var tester = new ContDistTester(para[i], delegate(double a, double b)
{
var ret = new ParetoDistribution
{
Alpha = a,
Beta = b
};
return(ret);
}
);
tester.Test(1E-14);
}
}
public void MedianTest()
{
var target = new ParetoDistribution(scale: 7.12, shape: 2);
Assert.AreEqual(target.Median, target.InverseDistributionFunction(0.5), 1e-6);
}
/// <summary>
/// Sets the distribution for operations using the current genrator
/// </summary>
/// <param name="distx">Distx.</param>
public void setDistribution(distributions distx, Dictionary <string, double> args)
{
//TODO check arguments to ensure they are making a change to the distribution
//otherwise throw an exception see laplace as a example of implementing this
switch (distx)
{
case distributions.Bernoili:
BernoulliDistribution x0 = new BernoulliDistribution(gen);
if (args.ContainsKey("alpha"))
{
x0.Alpha = args["alpha"];
}
else
{
throw new System.Exception("for Bernoili distribution you must provide an alpha");
}
dist = x0;
break;
case distributions.Beta:
BetaDistribution x1 = new BetaDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("beta"))
{
x1.Alpha = args["alpha"];
x1.Beta = args["beta"];
}
else
{
throw new System.Exception(" for beta distribution you must provide alpha and beta");
}
dist = x1;
break;
case distributions.BetaPrime:
BetaPrimeDistribution x2 = new BetaPrimeDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("beta"))
{
x2.Alpha = args["alpha"];
x2.Beta = args["beta"];
}
else
{
throw new System.Exception(" for betaPrime distribution you must provide alpha and beta");
}
dist = x2;
break;
case distributions.Cauchy:
CauchyDistribution x3 = new CauchyDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("gamma"))
{
x3.Alpha = args["alpha"];
x3.Gamma = args["gamma"];
}
else
{
throw new System.Exception("for cauchy dist you must provide alpha and gamma");
}
dist = x3;
break;
case distributions.Chi:
ChiDistribution x4 = new ChiDistribution(gen);
if (args.ContainsKey("alpha"))
{
x4.Alpha = (int)args["alpha"];
}
else
{
throw new System.Exception("for chi you must provide alpha");
}
dist = x4;
break;
case distributions.ChiSquared:
ChiSquareDistribution x5 = new ChiSquareDistribution(gen);
if (args.ContainsKey("alpha"))
{
x5.Alpha = (int)args["alpha"];
}
else
{
throw new System.Exception("for chiSquared you must provide alpha");
}
dist = x5;
break;
case distributions.ContinuousUniform:
ContinuousUniformDistribution x6 = new ContinuousUniformDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("beta"))
{
x6.Alpha = args["alpha"];
x6.Beta = args["beta"];
}
else
{
throw new System.Exception("for ContinuousUniform you must provide alpha and beta");
}
dist = x6;
break;
case distributions.DiscreteUniform:
DiscreteUniformDistribution x7 = new DiscreteUniformDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("beta"))
{
x7.Alpha = (int)args["alpha"];
x7.Beta = (int)args["beta"];
}
else
{
throw new System.Exception("for discrete uniform distribution you must provide alpha and beta");
}
dist = x7;
break;
case distributions.Erlang:
ErlangDistribution x8 = new ErlangDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("lambda"))
{
x8.Alpha = (int)args["alpha"];
x8.Lambda = (int)args["lambda"];
}
else
{
throw new System.Exception("for Erlang dist you must provide alpha and lambda");
}
dist = x8;
break;
case distributions.Exponential:
ExponentialDistribution x9 = new ExponentialDistribution(gen);
if (args.ContainsKey("lambda"))
{
x9.Lambda = args["lambda"];
}
else
{
throw new System.Exception("for exponential dist you must provide lambda");
}
dist = x9;
break;
case distributions.FisherSnedecor:
FisherSnedecorDistribution x10 = new FisherSnedecorDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("beta"))
{
x10.Alpha = (int)args["alpha"];
x10.Beta = (int)args["beta"];
}
else
{
throw new System.Exception("for FisherSnedecor you must provide alpha and beta");
}
dist = x10;
break;
case distributions.FisherTippett:
FisherTippettDistribution x11 = new FisherTippettDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("mu"))
{
x11.Alpha = args["alpha"];
x11.Mu = args["mu"];
}
else
{
throw new System.Exception("for FisherTippets you must provide alpha and mu");
}
dist = x11;
break;
case distributions.Gamma:
GammaDistribution x12 = new GammaDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("theta"))
{
x12.Alpha = args["alpha"];
x12.Theta = args["theta"];
}
else
{
throw new System.Exception("for Gamma dist you must provide alpha and theta");
}
dist = x12;
break;
case distributions.Geometric:
GeometricDistribution x13 = new GeometricDistribution(gen);
if (args.ContainsKey("alpha"))
{
x13.Alpha = args["alpha"];
}
else
{
throw new System.Exception("Geometric distribution requires alpha value");
}
dist = x13;
break;
case distributions.Binomial:
BinomialDistribution x14 = new BinomialDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("beta"))
{
x14.Alpha = args["alpha"];
x14.Beta = (int)args["beta"];
}
else
{
throw new System.Exception("binomial distribution requires alpha and beta");
}
dist = x14;
break;
case distributions.None:
break;
case distributions.Laplace:
LaplaceDistribution x15 = new LaplaceDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("mu"))
{
if (x15.IsValidAlpha(args["alpha"]) && x15.IsValidMu(args["mu"]))
{
x15.Alpha = args["alpha"];
x15.Mu = args["mu"];
}
else
{
throw new ArgumentException("alpha must be greater than zero");
}
}
else
{
throw new System.Exception("Laplace dist requires alpha and mu");
}
dist = x15;
break;
case distributions.LogNormal:
LognormalDistribution x16 = new LognormalDistribution(gen);
if (args.ContainsKey("mu") && args.ContainsKey("sigma"))
{
x16.Mu = args["mu"];
x16.Sigma = args["sigma"];
}
else
{
throw new System.Exception("lognormal distribution requires mu and sigma");
}
dist = x16;
break;
case distributions.Normal:
NormalDistribution x17 = new NormalDistribution(gen);
if (args.ContainsKey("mu") && args.ContainsKey("sigma"))
{
x17.Mu = args["mu"];
x17.Sigma = args["sigma"];
}
else
{
throw new System.Exception("normal distribution requires mu and sigma");
}
dist = x17;
break;
case distributions.Pareto:
ParetoDistribution x18 = new ParetoDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("beta"))
{
x18.Alpha = args["alpha"];
x18.Beta = args["beta"];
}
else
{
throw new System.Exception("pareto distribution requires alpha and beta");
}
dist = x18;
break;
case distributions.Poisson:
PoissonDistribution x19 = new PoissonDistribution(gen);
if (args.ContainsKey("lambda"))
{
x19.Lambda = args["lambda"];
}
else
{
throw new System.Exception("Poisson distribution requires lambda");
}
dist = x19;
break;
case distributions.Power:
PowerDistribution x20 = new PowerDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("beta"))
{
x20.Alpha = args["alpha"];
x20.Beta = args["beta"];
}
else
{
throw new System.Exception("Power dist requires alpha and beta");
}
dist = x20;
break;
case distributions.RayLeigh:
RayleighDistribution x21 = new RayleighDistribution(gen);
if (args.ContainsKey("sigma"))
{
x21.Sigma = args["sigma"];
}
else
{
throw new System.Exception("Rayleigh dist requires sigma");
}
dist = x21;
break;
case distributions.StudentsT:
StudentsTDistribution x22 = new StudentsTDistribution(gen);
if (args.ContainsKey("nu"))
{
x22.Nu = (int)args["nu"];
}
else
{
throw new System.Exception("StudentsT dist requirres nu");
}
dist = x22;
break;
case distributions.Triangular:
TriangularDistribution x23 = new TriangularDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("beta") && args.ContainsKey("gamma"))
{
x23.Alpha = args["alpha"];
x23.Beta = args["beta"];
x23.Gamma = args["gamma"];
}
else
{
throw new System.Exception("Triangular distribution requires alpha, beta and gamma");
}
dist = x23;
break;
case distributions.WeiBull:
WeibullDistribution x24 = new WeibullDistribution(gen);
if (args.ContainsKey("alpha") && args.ContainsKey("lambda"))
{
x24.Alpha = args["alpha"];
x24.Lambda = args["lambda"];
}
else
{
throw new System.Exception("WeiBull dist requires alpha and lambda");
}
dist = x24;
break;
default:
throw new NotImplementedException("the distribution you want has not yet been implemented " +
"you could help everyone out by going and implementing it");
}
}
