public double[] getSamples(int num) // 获取指定个数的样本
{
double[] ret = new double[num];
int[] ret_int = new int[num];
switch (DistributionName)
{
case "Normal":
normalDis.Samples(ret);
break;
case "ContinuousUniform":
continuousUniformDis.Samples(ret);
break;
case "Triangular":
triangularDis.Samples(ret);
break;
case "StudentT":
studentTDis.Samples(ret);
break;
case "DiscreteUniform":
discreteUniform.Samples(ret_int);
for (int i = 0; i < num; i++)
{
ret[i] = ret_int[i];
}
break;
}
return(ret);
}
public void CanSampleSequence()
{
var n = new StudentT();
var ied = n.Samples();
ied.Take(5).ToArray();
}
public static double[] studentT(double v1, double v2, double v3, int num)
{
var t = new StudentT(v1, v2, v3);
double[] ret = new double[num];
t.Samples(ret);
return(ret);
}
public void FailSampleSequenceStatic(double location, double scale, double dof)
{
var ied = StudentT.Samples(new Random(), location, scale, dof);
var e = ied.Take(5).ToArray();
}
public void CanSampleSequenceStatic()
{
var ied = StudentT.Samples(new Random(), 0.0, 1.0, 3.0);
var arr = ied.Take(5).ToArray();
}
public void FailSampleSequenceStatic()
{
var ied = StudentT.Samples(new Random(0), 0.0, 1.0, Double.NaN);
Assert.Throws <ArgumentOutOfRangeException>(() => ied.Take(5).ToArray());
}
public void CanSampleSequenceStatic()
{
var ied = StudentT.Samples(new Random(0), 0.0, 1.0, 3.0);
GC.KeepAlive(ied.Take(5).ToArray());
}
public void CanSampleSequence()
{
var n = new StudentT();
var ied = n.Samples();
var e = ied.Take(5).ToArray();
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/StudentT_distribution">StudentT distribution</a>
public void Run()
{
// 1. Initialize the new instance of the StudentT distribution class with parameters Location = 0, Scale = 1, DegreesOfFreedom = 1
var studentT = new StudentT();
Console.WriteLine(@"1. Initialize the new instance of the StudentT distribution class with parameters Location = {0}, Scale = {1}, DegreesOfFreedom = {2}", studentT.Location, studentT.Scale, studentT.DegreesOfFreedom);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", studentT);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", studentT.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability density at location '0.3'", studentT.Density(0.3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability density at location '0.3'", studentT.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", studentT.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", studentT.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", studentT.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", studentT.Mean.ToString(" #0.00000;-#0.00000"));
// Median
Console.WriteLine(@"{0} - Median", studentT.Median.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", studentT.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", studentT.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", studentT.StdDev.ToString(" #0.00000;-#0.00000"));
// 3. Generate 10 samples of the StudentT distribution
Console.WriteLine(@"3. Generate 10 samples of the StudentT distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(studentT.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the StudentT(0, 1, 1) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the StudentT(0, 1, 1) distribution and display histogram");
var data = new double[100000];
StudentT.Samples(data, 0, 1, 1);
ConsoleHelper.DisplayHistogram(data);
// 5. Generate 100000 samples of the StudentT(0, 1, 5) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the StudentT(0, 1, 5) distribution and display histogram");
StudentT.Samples(data, 0, 1, 5);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the StudentT(0, 1, 10) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the StudentT(0, 1, 10) distribution and display histogram");
StudentT.Samples(data, 0, 1, 10);
ConsoleHelper.DisplayHistogram(data);
}
