//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 Constructor_ExtensiveTestForDocumentation()
{
// Create a new Beta-Prime distribution with shape (4,2)
var betaPrime = new BetaPrimeDistribution(alpha: 4, beta: 2);
double mean = betaPrime.Mean; // 4.0
double median = betaPrime.Median; // 2.1866398762435981
double mode = betaPrime.Mode; // 1.0
double var = betaPrime.Variance; // +inf
double cdf = betaPrime.DistributionFunction(x: 0.4); // 0.02570357589099781
double pdf = betaPrime.ProbabilityDensityFunction(x: 0.4); // 0.16999719504628183
double lpdf = betaPrime.LogProbabilityDensityFunction(x: 0.4); // -1.7719733417957513
double ccdf = betaPrime.ComplementaryDistributionFunction(x: 0.4); // 0.97429642410900219
double icdf = betaPrime.InverseDistributionFunction(p: cdf); // 0.39999982363709291
double hf = betaPrime.HazardFunction(x: 0.4); // 0.17448200654307533
double chf = betaPrime.CumulativeHazardFunction(x: 0.4); // 0.026039684773113869
string str = betaPrime.ToString(CultureInfo.InvariantCulture); // BetaPrime(x; α = 4, β = 2)
Assert.AreEqual(4, betaPrime.Alpha);
Assert.AreEqual(2, betaPrime.Beta);
Assert.AreEqual(4.0, mean);
Assert.AreEqual(2.1866398762435981, median);
Assert.AreEqual(1.0, mode);
Assert.AreEqual(double.PositiveInfinity, var);
Assert.AreEqual(0.026039684773113869, chf);
Assert.AreEqual(0.02570357589099781, cdf);
Assert.AreEqual(0.16999719504628183, pdf);
Assert.AreEqual(-1.7719733417957513, lpdf);
Assert.AreEqual(0.17448200654307533, hf);
Assert.AreEqual(0.97429642410900219, ccdf);
Assert.AreEqual(0.39999982363709291, icdf);
Assert.AreEqual("BetaPrime(x; α = 4, β = 2)", str);
var range1 = betaPrime.GetRange(0.95);
var range2 = betaPrime.GetRange(0.99);
var range3 = betaPrime.GetRange(0.01);
Assert.AreEqual(0.52112465307247502, range1.Min);
Assert.AreEqual(12.082089043372052, range1.Max);
Assert.AreEqual(0.28546647531958014, range2.Min);
Assert.AreEqual(29.597777621141635, range2.Max);
Assert.AreEqual(range2.Min, range3.Min, 1e-15);
Assert.AreEqual(range2.Max, range3.Max, 1e-15);
}
public void Constructor_ExtensiveTestForDocumentation()
{
// Create a new Beta-Prime distribution with shape (4,2)
var betaPrime = new BetaPrimeDistribution(alpha: 4, beta: 2);
double mean = betaPrime.Mean; // 4.0
double median = betaPrime.Median; // 2.1866398762435981
double mode = betaPrime.Mode; // 1.0
double var = betaPrime.Variance; // +inf
double cdf = betaPrime.DistributionFunction(x: 0.4); // 0.02570357589099781
double pdf = betaPrime.ProbabilityDensityFunction(x: 0.4); // 0.16999719504628183
double lpdf = betaPrime.LogProbabilityDensityFunction(x: 0.4); // -1.7719733417957513
double ccdf = betaPrime.ComplementaryDistributionFunction(x: 0.4); // 0.97429642410900219
double icdf = betaPrime.InverseDistributionFunction(p: cdf); // 0.39999982363709291
double hf = betaPrime.HazardFunction(x: 0.4); // 0.17448200654307533
double chf = betaPrime.CumulativeHazardFunction(x: 0.4); // 0.026039684773113869
string str = betaPrime.ToString(CultureInfo.InvariantCulture); // BetaPrime(x; α = 4, β = 2)
Assert.AreEqual(4, betaPrime.Alpha);
Assert.AreEqual(2, betaPrime.Beta);
Assert.AreEqual(4.0, mean);
Assert.AreEqual(2.1866398762435981, median);
Assert.AreEqual(1.0, mode);
Assert.AreEqual(double.PositiveInfinity, var);
Assert.AreEqual(0.026039684773113869, chf);
Assert.AreEqual(0.02570357589099781, cdf);
Assert.AreEqual(0.16999719504628183, pdf);
Assert.AreEqual(-1.7719733417957513, lpdf);
Assert.AreEqual(0.17448200654307533, hf);
Assert.AreEqual(0.97429642410900219, ccdf);
Assert.AreEqual(0.39999982363709291, icdf);
Assert.AreEqual("BetaPrime(x; α = 4, β = 2)", str);
var range1 = betaPrime.GetRange(0.95);
var range2 = betaPrime.GetRange(0.99);
var range3 = betaPrime.GetRange(0.01);
Assert.AreEqual(0.52112465307247502, range1.Min);
Assert.AreEqual(12.082089043372052, range1.Max);
Assert.AreEqual(0.28546647531958014, range2.Min);
Assert.AreEqual(29.597777621141635, range2.Max);
Assert.AreEqual(range2.Min, range3.Min, 1e-15);
Assert.AreEqual(range2.Max, range3.Max, 1e-15);
}
public void Confirm_BetPrimeDistribution_Relative_to_F_Distribution()
{
double alpha = 4.0d;
double beta = 6.0d;
FDistribution fdist = new FDistribution((int)alpha * 2, (int)beta * 2);
double fMean = fdist.Mean;
double fPdf = (beta / alpha) * fdist.ProbabilityDensityFunction(4.0d);
double fCdf = fdist.DistributionFunction(4.0d);
var betaPrimeDist = new BetaPrimeDistribution(alpha, beta);
double bpMean = (beta / alpha) * betaPrimeDist.Mean;
double bpPdf = betaPrimeDist.ProbabilityDensityFunction((alpha / beta) * 4.0d);
double bpCdf = betaPrimeDist.DistributionFunction((alpha / beta) * 4.0d);
Assert.AreEqual(fMean, bpMean, 0.00000001, "mean should be equal");
Assert.AreEqual(fPdf, bpPdf, 0.00000001, "probability density should be equal");
Assert.AreEqual(fCdf, bpCdf, 0.00000001, "cumulative distribution should be equal");
//Beta Prime distribution is a scaled version of Pearson Type VI, which itself is scale of F distribution
}
public void Confirm_BetPrimeDistribution_Relative_to_F_Distribution()
{
double alpha = 4.0d;
double beta = 6.0d;
FDistribution fdist = new FDistribution((int)alpha * 2, (int)beta * 2);
double fMean = fdist.Mean;
double fPdf = (beta / alpha) * fdist.ProbabilityDensityFunction(4.0d);
double fCdf = fdist.DistributionFunction(4.0d);
var betaPrimeDist = new BetaPrimeDistribution(alpha, beta);
double bpMean = (beta / alpha) * betaPrimeDist.Mean;
double bpPdf = betaPrimeDist.ProbabilityDensityFunction((alpha / beta) * 4.0d);
double bpCdf = betaPrimeDist.DistributionFunction((alpha / beta) * 4.0d);
Assert.AreEqual(fMean, bpMean, 0.00000001, "mean should be equal");
Assert.AreEqual(fPdf, bpPdf, 0.00000001, "probability density should be equal");
Assert.AreEqual(fCdf, bpCdf, 0.00000001, "cumulative distribution should be equal");
//Beta Prime distribution is a scaled version of Pearson Type VI, which itself is scale of F distribution
}
/// <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");
}
}
