public override double[] GenerateSequence(int n = N)
{
var norm = new NormalDG().GenerateSequence();
double[] res = new double[n].Select((x, i) => U * Math.Exp(U * (Math.Log(U) + Math.Sqrt(S) * norm[i]))).ToArray();
return(res);
}
static void Main(string[] args)
{
var NormalDG = new NormalDG();
var ExponentialDG = new ExponentialDG();
var LogisticDG = new LogisticDG();
var CauchyDG = new CauchyDG();
var StudentsDG = new StudentsDG();
string Format(double val)
{
return(string.Format("{0:0.###}", val));
}
double[] PrintDGMainInfo(DistributionGenerator dg, string dg_name)
{
var arr = dg.GenerateSequence();
if (dg.GetType() != typeof(CauchyDG))
{
DistributionGenerator.GetRealExpectedValueAndDispersion(arr, out double expval, out double disp);
Console.WriteLine($"[{dg_name}] \n" +
$"Expected value: {Format(dg.ExpectedValue)} Real: {Format(expval)}\n" +
$"Dispersion: {Format(dg.Dispersion)} Real: {Format(disp)}\n\n");
}
else
{
CauchyDG cauchyDG = (CauchyDG)dg;
Console.WriteLine($"[{dg_name}] \n" +
$"Selective median: {Format(CauchyDG.SMV)} Real: {Format(cauchyDG.CalculateSMV(arr))}\n\n");
}
return(arr);
}
Console.WriteLine("[==================[Main task]==================]");
PrintDGMainInfo(NormalDG, "NormalDG");
PrintDGMainInfo(ExponentialDG, "ExponentialDG");
PrintDGMainInfo(LogisticDG, "LogisticDG");
PrintDGMainInfo(CauchyDG, "CauchyDG");
PrintDGMainInfo(StudentsDG, "StudentsDG");
var MixedExponentialLogisticDG = new MixedExponentialLogisticDG();
var BoxMullerDG = new BoxMullerDG();
var TruncatedExponentialDG = new TruncatedExponentialDG(1.9, 2.1);
BoxMullerDG.GenerateSequence(out double corr);
Console.WriteLine("[==================[Additional tasks]==================]");
PrintDGMainInfo(MixedExponentialLogisticDG, "MixedExponentialLogisticDG");
PrintDGMainInfo(TruncatedExponentialDG, "TruncatedExponentialDistribution(a = 1.9, b = 2.1)");
Console.WriteLine($"[BoxMullerDistribution] \nEven/odd correlation: {Format(corr)}");
Console.Read();
}
public override double[] GenerateSequence(int n = N)
{
double[] norm = new NormalDG().GenerateSequence();
double[] res = new double[n]
.Select(_ => norm[Random.Next(0, n)] * Math.Sqrt(Enumerable.Repeat(0, M)
.Select(y => Math.Pow(norm[Random.Next(0, n)], 2)).Sum() / M))
.ToArray();
return(res);
}
