public void CanSampleSequence()
{
var n = new Pareto(1.0, 1.0);
var ied = n.Samples();
ied.Take(5).ToArray();
}
public void CanSampleSequence()
{
var n = new Pareto(1.0, 1.0);
var ied = n.Samples();
ied.Take(5).ToArray();
}
public double[] GetSampleData(string distType, double mostLikelyEstimate,
double lowEstimate, double highEstimate)
{
if (Iterations > 10000)
{
Iterations = 10000;
}
if (Iterations <= 2)
{
Iterations = 1000;
}
if (this.CILevel < 10)
{
this.CILevel = 90;
}
if (this.CILevel > 99)
{
this.CILevel = 99;
}
Random rnd = new Random(Random);
mostLikelyEstimate = Math.Round(mostLikelyEstimate, 4);
lowEstimate = Math.Round(lowEstimate, 4);
highEstimate = Math.Round(highEstimate, 4);
var sampledata = new double[Iterations];
if (distType == Calculator1.RUC_TYPES.triangle.ToString())
{
if (lowEstimate >= mostLikelyEstimate || lowEstimate == 0)
{
//arbitrary rules (25%)
lowEstimate = mostLikelyEstimate * .75;
//no errors: lowEstimate = 0 is often the case
//sb.AppendLine(Errors.GetMessage("DATA_BADDISTRIBUTION"));
}
if (highEstimate <= mostLikelyEstimate || highEstimate == 0)
{
//arbitrary rules (25%)
highEstimate = mostLikelyEstimate * 1.25;
}
if (Random != 0)
{
//generate samples of the Triangular(low, high, mode) distribution;
Triangular.Samples(rnd, sampledata, lowEstimate, highEstimate, mostLikelyEstimate);
}
else
{
//generate samples of the Triangular(low, high, mode) distribution;
Triangular.Samples(sampledata, lowEstimate, highEstimate, mostLikelyEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.normal.ToString())
{
//generate samples of the Normal(mean, sd) distribution;
if (Random != 0)
{
Normal.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Normal.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.lognormal.ToString())
{
if (Random != 0)
{
LogNormal.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
LogNormal.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.weibull.ToString())
{
if (Random != 0)
{
Weibull.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Weibull.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.beta.ToString())
{
if (Random != 0)
{
Beta.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Beta.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.pareto.ToString())
{
if (Random != 0)
{
Pareto.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Pareto.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.uniform.ToString())
{
var sampleints = new int[Iterations];
int iLower = CalculatorHelpers.ConvertStringToInt(lowEstimate.ToString());
int iUpper = CalculatorHelpers.ConvertStringToInt(highEstimate.ToString());
if (Random != 0)
{
DiscreteUniform.Samples(rnd, sampleints, iLower, iUpper);
}
else
{
DiscreteUniform.Samples(sampleints, iLower, iUpper);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.bernoulli.ToString())
{
var sampleints = new int[Iterations];
if (Random != 0)
{
Bernoulli.Samples(rnd, sampleints, lowEstimate);
}
else
{
Bernoulli.Samples(sampleints, lowEstimate);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.poisson.ToString())
{
var sampleints = new int[Iterations];
if (Random != 0)
{
Poisson.Samples(rnd, sampleints, lowEstimate);
}
else
{
Poisson.Samples(sampleints, lowEstimate);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.binomial.ToString())
{
var sampleints = new int[Iterations];
int iUpperEstimate = CalculatorHelpers.ConvertStringToInt(highEstimate.ToString());
if (Random != 0)
{
Binomial.Samples(rnd, sampleints, lowEstimate, iUpperEstimate);
}
else
{
Binomial.Samples(sampleints, lowEstimate, iUpperEstimate);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.gamma.ToString())
{
//generate samples of the Gamma(shape, scale) distribution;
if (Random != 0)
{
Gamma.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Gamma.Samples(sampledata, lowEstimate, highEstimate);
}
}
else
{
//don't force them to use distribution
}
//hold for possible infernet use
//else if (distType == Calculator1.RUC_TYPES.dirichlet.ToString())
//{
// //generate samples of the Dirichlet(random, alpha) distribution;
// Dirichlet.Sample(sampledata, lowEstimate);
//}
//else if (distType == Calculator1.RUC_TYPES.wishart.ToString())
//{
// //generate samples of the Wishart(random, degrees of freedom, scale) distribution;
// Wishart.Sample(sampledata, lowEstimate, highEstimate);
//}
//the mathlibrary supports more than a dozen additional distributions
return(sampledata);
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Pareto_distribution">Pareto distribution</a>
public void Run()
{
// 1. Initialize the new instance of the Pareto distribution class with parameters Shape = 3, Scale = 1
var pareto = new Pareto(1, 3);
Console.WriteLine(@"1. Initialize the new instance of the Pareto distribution class with parameters Shape = {0}, Scale = {1}", pareto.Shape, pareto.Scale);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", pareto);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", pareto.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability density at location '0.3'", pareto.Density(0.3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability density at location '0.3'", pareto.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", pareto.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", pareto.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", pareto.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", pareto.Mean.ToString(" #0.00000;-#0.00000"));
// Median
Console.WriteLine(@"{0} - Median", pareto.Median.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", pareto.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", pareto.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", pareto.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", pareto.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the Pareto distribution
Console.WriteLine(@"3. Generate 10 samples of the Pareto distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(pareto.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the Pareto(1, 3) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the Pareto(1, 3) distribution and display histogram");
var data = new double[100000];
Pareto.Samples(data, 1.0, 3.0);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the Pareto(1, 1) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the Pareto(1, 1) distribution and display histogram");
Pareto.Samples(data, 1.0, 1.0);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the Pareto(10, 5) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the Pareto(10, 50) distribution and display histogram");
Pareto.Samples(data, 10.0, 50.0);
ConsoleHelper.DisplayHistogram(data);
}
