public void CanCreateHypergeometric(int size, int m, int n)
{
var d = new Hypergeometric(size, m, n);
Assert.AreEqual(size, d.PopulationSize);
Assert.AreEqual(m, d.M);
Assert.AreEqual(n, d.N);
}
public void ValidateToString()
{
System.Threading.Thread.CurrentThread.CurrentCulture = System.Globalization.CultureInfo.InvariantCulture;
var d = new Hypergeometric(10, 1, 1);
Assert.AreEqual("Hypergeometric(N = 10, M = 1, n = 1)", d.ToString());
}
public static async Task <IActionResult> Run(
[HttpTrigger(AuthorizationLevel.Anonymous, "get", "post", Route = null)] HttpRequest req,
ILogger log)
{
log.LogInformation("C# HTTP trigger function processed a request.");
string populationSizeStr = req.Query["CardsRemaining"];
string successesStr = req.Query["Outs"];
string drawsStr = req.Query["Draws"];
string neededStr = req.Query["Needed"];
if (string.IsNullOrWhiteSpace(populationSizeStr) || string.IsNullOrWhiteSpace(successesStr) ||
string.IsNullOrWhiteSpace(drawsStr) || string.IsNullOrWhiteSpace(neededStr))
{
return(new BadRequestObjectResult("Missing Parameters"));
}
var popSuccess = int.TryParse(populationSizeStr, out var population);
var successesSuccess = int.TryParse(successesStr, out var successes);
var drawsSuccess = int.TryParse(drawsStr, out var draws);
var neededSuccess = int.TryParse(neededStr, out var needed);
if (!popSuccess || !successesSuccess || !drawsSuccess || !neededSuccess)
{
return(new BadRequestObjectResult("Numbers have to be an int"));
}
var dist = new Hypergeometric(population, successes, draws);
var result = 1 - dist.CumulativeDistribution(needed - 1);
return(new OkObjectResult(result.ToString(CultureInfo.InvariantCulture)));
}
public void CanCreateHypergeometric(int N, int m, int n)
{
var d = new Hypergeometric(N, m, n);
Assert.AreEqual<double>(N, d.PopulationSize);
Assert.AreEqual<double>(m, d.M);
Assert.AreEqual<double>(n, d.N);
}
public void CanCreateHypergeometric(int population, int success, int draws)
{
var d = new Hypergeometric(population, success, draws);
Assert.AreEqual(population, d.Population);
Assert.AreEqual(success, d.Success);
Assert.AreEqual(draws, d.Draws);
}
public void CumulativeDistributionMustNotOverflow_CodePlexIssue5729()
{
var d = new Hypergeometric(10000, 2, 9800);
Assert.That(d.CumulativeDistribution(0.0), Is.Not.NaN);
Assert.That(d.CumulativeDistribution(0.1), Is.Not.NaN);
}
public void CanSampleSequence()
{
var d = new Hypergeometric(10, 1, 1);
var ied = d.Samples();
ied.Take(5).ToArray();
}
public void CanCreateHypergeometric([Values(0, 1, 2, 2, 10, 10)] int size, [Values(0, 1, 1, 2, 1, 5)] int m, [Values(0, 1, 1, 2, 1, 3)] int n)
{
var d = new Hypergeometric(size, m, n);
Assert.AreEqual(size, d.PopulationSize);
Assert.AreEqual(m, d.M);
Assert.AreEqual(n, d.N);
}
public static double CalculateWeight(int N, int M, int n, int k)
{
if (M < k)
{
return(0);
}
return(Hypergeometric.CDF(N, M, n, k));
}
public void CanCreateHypergeometric([Values(0, 1, 2, 2, 10, 10)] int size, [Values(0, 1, 1, 2, 1, 5)] int m, [Values(0, 1, 1, 2, 1, 3)] int n)
{
var d = new Hypergeometric(size, m, n);
Assert.AreEqual(size, d.PopulationSize);
Assert.AreEqual(m, d.M);
Assert.AreEqual(n, d.N);
}
public void CanCreateHypergeometric(int population, int success, int draws)
{
var d = new Hypergeometric(population, success, draws);
Assert.AreEqual(population, d.Population);
Assert.AreEqual(success, d.Success);
Assert.AreEqual(draws, d.Draws);
}
public void CanCreateHypergeometric(int N, int m, int n)
{
var d = new Hypergeometric(N, m, n);
Assert.AreEqual <double>(N, d.PopulationSize);
Assert.AreEqual <double>(m, d.M);
Assert.AreEqual <double>(n, d.N);
}
public void CanCreateHypergeometric(int size, int m, int n)
{
var d = new Hypergeometric(size, m, n);
Assert.AreEqual(size, d.PopulationSize);
Assert.AreEqual(m, d.M);
Assert.AreEqual(n, d.N);
}
public void ValidateProbabilityLn(
[Values(0, 1, 2, 2, 2, 10, 10, 10, 10)] int size,
[Values(0, 1, 1, 1, 2, 1, 1, 5, 5)] int m,
[Values(0, 1, 1, 1, 2, 1, 1, 3, 3)] int n,
[Values(0, 1, 0, 1, 2, 0, 1, 1, 3)] int x)
{
var d = new Hypergeometric(size, m, n);
Assert.AreEqual(Math.Log(d.Probability(x)), d.ProbabilityLn(x));
}
public void ValidateProbability(
[Values(0, 1, 2, 2, 2, 10, 10, 10, 10)] int size,
[Values(0, 1, 1, 1, 2, 1, 1, 5, 5)] int m,
[Values(0, 1, 1, 1, 2, 1, 1, 3, 3)] int n,
[Values(0, 1, 0, 1, 2, 0, 1, 1, 3)] int x)
{
var d = new Hypergeometric(size, m, n);
Assert.AreEqual(SpecialFunctions.Binomial(m, x) * SpecialFunctions.Binomial(size - m, n - x) / SpecialFunctions.Binomial(size, n), d.Probability(x));
}
public void ValidateCumulativeDistribution(
[Values(0, 1, 2, 2, 2, 10, 10, 10, 10)] int size,
[Values(0, 1, 1, 1, 2, 1, 1, 5, 5)] int m,
[Values(0, 1, 1, 1, 2, 1, 1, 3, 3)] int n,
[Values(0.5, 1.1, 0.3, 1.2, 2.4, 0.3, 1.2, 1.1, 3.0)] double x,
[Values(1.0, 1.0, 0.5, 1.0, 1.0, 0.9, 1.0, 0.5, 0.916666666666667)] double cdf)
{
var d = new Hypergeometric(size, m, n);
AssertHelpers.AlmostEqual(cdf, d.CumulativeDistribution(x), 14);
}
private static void hypergeometric_cdf_test()
//****************************************************************************80
//
// Purpose:
//
// HYPERGEOMETRIC_CDF_TEST tests HYPERGEOMETRIC_CDF.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 08 April 2016
//
// Author:
//
// John Burkardt
//
{
Console.WriteLine("");
Console.WriteLine("HYPERGEOMETRIC_CDF_TEST");
Console.WriteLine(" HYPERGEOMETRIC_CDF evaluates the Hypergeometric CDF.");
Console.WriteLine(" HYPERGEOMETRIC_PDF evaluates the Hypergeometric PDF.");
const int n = 10;
const int m = 7;
const int l = 100;
Console.WriteLine("");
Console.WriteLine(" Total number of balls L = " + l + "");
Console.WriteLine(" Number of white balls M = " + m + "");
Console.WriteLine(" Number of balls taken N = " + n + "");
if (!Hypergeometric.hypergeometric_check(n, m, l))
{
Console.WriteLine("");
Console.WriteLine("HYPERGEOMETRIC_CDF_TEST - Fatal error!");
Console.WriteLine(" The parameters are not legal.");
return;
}
int x = 7;
double pdf = Hypergeometric.hypergeometric_pdf(x, n, m, l);
double cdf = Hypergeometric.hypergeometric_cdf(x, n, m, l);
Console.WriteLine(" PDF argument X = " + x + "");
Console.WriteLine(" PDF value = = " + pdf + "");
Console.WriteLine(" CDF value = = " + cdf + "");
}
public static void hypergeometric_pdf_values_test()
//****************************************************************************80
//
// Purpose:
//
// HYPERGEOMETRIC_PDF_VALUES_TEST tests HYPERGEOMETRIC_PDF_VALUES.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 28 January 2008
//
// Author:
//
// John Burkardt
//
{
double fx = 0;
int pop = 0;
int sam = 0;
int succ = 0;
int x = 0;
Console.WriteLine("");
Console.WriteLine("HYPERGEOMETRIC_PDF_VALUES_TEST:");
Console.WriteLine(" HYPERGEOMETRIC_PDF_VALUES stores values of");
Console.WriteLine(" the Hypergeometric PDF.");
Console.WriteLine("");
Console.WriteLine(" SAM SUC POP X HyperPDF(S,S,P)(X)");
Console.WriteLine("");
int n_data = 0;
for (;;)
{
Hypergeometric.hypergeometric_pdf_values(ref n_data, ref sam, ref succ, ref pop, ref x, ref fx);
if (n_data == 0)
{
break;
}
Console.WriteLine(" "
+ sam.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ succ.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ pop.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ x.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ fx.ToString("0.################").PadLeft(24) + "");
}
}
public static void hyper_2f1_values_test()
//****************************************************************************80
//
// Purpose:
//
// HYPER_2F1_VALUES_TEST tests HYPER_2F1_VALUES.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 09 September 2007
//
// Author:
//
// John Burkardt
//
{
double a = 0;
double b = 0;
double c = 0;
double fx = 0;
double x = 0;
Console.WriteLine("");
Console.WriteLine("HYPER_2F1_VALUES_TEST:");
Console.WriteLine(" HYPER_2F1_VALUES stores values of");
Console.WriteLine(" the hypergeometric function 2F1.");
Console.WriteLine("");
Console.WriteLine(" A B C X Hyper_2F1(A,B,C,X)");
Console.WriteLine("");
int n_data = 0;
for (;;)
{
Hypergeometric.hyper_2f1_values(ref n_data, ref a, ref b, ref c, ref x, ref fx);
if (n_data == 0)
{
break;
}
Console.WriteLine(" "
+ a.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ b.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ c.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ x.ToString(CultureInfo.InvariantCulture).PadLeft(8) + " "
+ fx.ToString("0.################").PadLeft(24) + "");
}
}
public static double GetHyperGeometricPvalue(int n, int k, int n1, int k1, bool upperTailProb = true)
{
if (k >= n)
{
return(1.0d);
}
var pValue = Hypergeometric.CDF(n, k, n1, k1);
if (upperTailProb)
{
pValue = 1 - pValue;
}
else
{
pValue = Math.Min(pValue, 1 - pValue);
}
return(pValue);
}
public void CalculateHypergeometric()
{
if (SamplesCount > this.PopulationSize || this.SuccessesCount > this.PopulationSize)
{
return;
}
Hypergeometric dist = new Hypergeometric(this.PopulationSize, this.SuccessesCount, this.SamplesCount);
LineChart cdfchart = new LineChart()
{
Entries = Enumerable.Range(0, this.SamplesCount + 1).Select(i =>
new Microcharts.Entry(1 - (float)dist.CumulativeDistribution(i - 1))
{
Label = i.ToString(),
ValueLabel = (100 - dist.CumulativeDistribution(i - 1) * 100).ToString("0.00") + "%"
}),
MinValue = 0,
MaxValue = 1,
LineMode = LineMode.Straight
};
this.HyperGeometricCdfChart.Chart = cdfchart;
LineChart pdfchart = new LineChart()
{
Entries = Enumerable.Range(0, this.SamplesCount + 1).Select(i =>
new Microcharts.Entry((float)dist.Probability(i))
{
Label = i.ToString(),
ValueLabel = (dist.Probability(i) * 100).ToString("0.00") + "%"
}),
MinValue = 0,
MaxValue = 1,
LineMode = LineMode.Straight
};
this.HyperGeometricPdfChart.Chart = pdfchart;
}
public void ValidateEntropy()
{
var d = new Hypergeometric(10, 1, 1);
var e = d.Entropy;
}
public void ValidateProbability(int N, int m, int n, int x)
{
var d = new Hypergeometric(N, m, n);
Assert.AreEqual<double>(SpecialFunctions.Binomial(m, x) * SpecialFunctions.Binomial(N - m, n - x) / SpecialFunctions.Binomial(N, n), d.Probability(x));
}
public void ValidateToString()
{
var d = new Hypergeometric(10, 1, 1);
Assert.AreEqual("Hypergeometric(N = 10, m = 1, n = 1)", d.ToString());
}
public void ValidateCumulativeDistribution(int size, int m, int n, double x, double cdf)
{
var d = new Hypergeometric(size, m, n);
AssertHelpers.AlmostEqual(cdf, d.CumulativeDistribution(x), 14);
}
public void CanSample()
{
var d = new Hypergeometric(10, 1, 1);
d.Sample();
}
public void CanSetSize(int N)
{
var d = new Hypergeometric(10, 1, 1);
d.PopulationSize = N;
}
public void CanSetn(int n)
{
var d = new Hypergeometric(10, 1, 1);
d.N = n;
}
public void CanSetm(int m)
{
var d = new Hypergeometric(10, 1, 1);
d.M = m;
}
public void ValidateSkewness(int N, int m, int n)
{
var d = new Hypergeometric(N, m, n);
Assert.AreEqual<double>((Math.Sqrt(N - 1.0) * (N - 2 * n) * (N - 2 * m)) / (Math.Sqrt(n * m * (N - m) * (N - n)) * (N - 2.0)), d.Skewness);
}
public void ValidateProbabilityLn(int N, int m, int n, int x)
{
var d = new Hypergeometric(N, m, n);
Assert.AreEqual(Math.Log(d.Probability(x)), d.ProbabilityLn(x));
}
public void ValidateProbability(int size, int m, int n, int x)
{
var d = new Hypergeometric(size, m, n);
Assert.AreEqual(SpecialFunctions.Binomial(m, x) * SpecialFunctions.Binomial(size - m, n - x) / SpecialFunctions.Binomial(size, n), d.Probability(x));
}
public void ValidateProbabilityLn(int size, int m, int n, int x)
{
var d = new Hypergeometric(size, m, n);
Assert.AreEqual(Math.Log(d.Probability(x)), d.ProbabilityLn(x));
}
public void ValidateMinimum(int population, int success, int draws)
{
var d = new Hypergeometric(population, success, draws);
Assert.AreEqual(Math.Max(0, draws + success - population), d.Minimum);
}
public void ValidateMinimum(int N, int m, int n)
{
var d = new Hypergeometric(N, m, n);
Assert.AreEqual(Math.Max(0, n + m - N), d.Minimum);
}
public void ValidateProbability(int population, int success, int draws, int x)
{
var d = new Hypergeometric(population, success, draws);
Assert.AreEqual(SpecialFunctions.Binomial(success, x)*SpecialFunctions.Binomial(population - success, draws - x)/SpecialFunctions.Binomial(population, draws), d.Probability(x));
}
public void ValidateMedian()
{
var d = new Hypergeometric(10, 1, 1);
var m = d.Median;
}
public void ValidateSkewness(int population, int success, int draws)
{
var d = new Hypergeometric(population, success, draws);
Assert.AreEqual((Math.Sqrt(population - 1.0)*(population - (2*draws))*(population - (2*success)))/(Math.Sqrt(draws*success*(population - success)*(population - draws))*(population - 2.0)), d.Skewness);
}
public void SetDrawsFails(int draws)
{
var d = new Hypergeometric(10, 1, 1);
Assert.Throws<ArgumentOutOfRangeException>(() => d.Draws = draws);
}
public void SetSuccessFails(int success)
{
var d = new Hypergeometric(10, 1, 1);
Assert.Throws<ArgumentOutOfRangeException>(() => d.Success = success);
}
private static void hypergeometric_sample_test()
//****************************************************************************80
//
// Purpose:
//
// HYPERGEOMETRIC_SAMPLE_TEST tests HYPERGEOMETRIC_SAMPLE.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 08 April 2016
//
// Author:
//
// John Burkardt
//
{
const int SAMPLE_NUM = 1000;
int j;
int seed = 123456789;
int[] x = new int[SAMPLE_NUM];
Console.WriteLine("");
Console.WriteLine("HYPERGEOMETRIC_SAMPLE_TEST");
Console.WriteLine(" HYPERGEOMETRIC_MEAN computes the Hypergeometric mean;");
Console.WriteLine(" HYPERGEOMETRIC_SAMPLE samples the Hypergeometric distribution;");
Console.WriteLine(" HYPERGEOMETRIC_VARIANCE computes the Hypergeometric variance.");
const int n = 10;
const int m = 7;
const int l = 100;
Console.WriteLine("");
Console.WriteLine(" Total number of balls L = " + l + "");
Console.WriteLine(" Number of white balls M = " + m + "");
Console.WriteLine(" Number of balls taken N = " + n + "");
if (!Hypergeometric.hypergeometric_check(n, m, l))
{
Console.WriteLine("");
Console.WriteLine("HYPERGEOMETRIC_SAMPLE_TEST - Fatal error!");
Console.WriteLine(" The parameters are not legal.");
return;
}
double mean = Hypergeometric.hypergeometric_mean(n, m, l);
double variance = Hypergeometric.hypergeometric_variance(n, m, l);
Console.WriteLine(" PDF mean = " + mean + "");
Console.WriteLine(" PDF variance = " + variance + "");
for (j = 0; j < SAMPLE_NUM; j++)
{
x[j] = Hypergeometric.hypergeometric_sample(n, m, l, ref seed);
}
mean = typeMethods.i4vec_mean(SAMPLE_NUM, x);
variance = typeMethods.i4vec_variance(SAMPLE_NUM, x);
int xmax = typeMethods.i4vec_max(SAMPLE_NUM, x);
int xmin = typeMethods.i4vec_min(SAMPLE_NUM, x);
Console.WriteLine("");
Console.WriteLine(" Sample size = " + SAMPLE_NUM + "");
Console.WriteLine(" Sample mean = " + mean + "");
Console.WriteLine(" Sample variance = " + variance + "");
Console.WriteLine(" Sample maximum = " + xmax + "");
Console.WriteLine(" Sample minimum = " + xmin + "");
}
public void ValidateEntropyThrowsNotSupportedException()
{
var d = new Hypergeometric(10, 1, 1);
Assert.Throws<NotSupportedException>(() => { var e = d.Entropy; });
}
public void CumulativeDistributionMustNotOverflow_CodePlexIssue5729()
{
var d = new Hypergeometric(10000, 2, 9800);
Assert.That(d.CumulativeDistribution(0.0), Is.Not.NaN);
Assert.That(d.CumulativeDistribution(0.1), Is.Not.NaN);
}
public void ValidateMode(int N, int m, int n)
{
var d = new Hypergeometric(N, m, n);
Assert.AreEqual<double>((n + 1) * (m + 1) / (N + 2), d.Mode);
}
public void SetPopulationFails(int population)
{
var d = new Hypergeometric(10, 1, 1);
Assert.Throws<ArgumentOutOfRangeException>(() => d.Population = population);
}
public void CanSampleSequence()
{
var d = new Hypergeometric(10, 1, 1);
var ied = d.Samples();
ied.Take(5).ToArray();
}
public void ValidateCumulativeDistribution(int population, int success, int draws, double x, double cdf)
{
var d = new Hypergeometric(population, success, draws);
AssertHelpers.AlmostEqual(cdf, d.CumulativeDistribution(x), 10);
}
public void ValidateMaximum(int size, int m, int n)
{
var d = new Hypergeometric(size, m, n);
Assert.AreEqual(Math.Min(m, n), d.Maximum);
}
public void ValidateMedianThrowsNotSupportedException()
{
var d = new Hypergeometric(10, 1, 1);
Assert.Throws<NotSupportedException>(() => { var m = d.Median; });
}
public void ValidateCumulativeDistribution(int N, int m, int n, double x, double cdf)
{
var d = new Hypergeometric(N, m, n);
AssertHelpers.AlmostEqual(cdf, d.CumulativeDistribution(x), 14);
}
public void ValidateMode(int population, int success, int draws)
{
var d = new Hypergeometric(population, success, draws);
Assert.AreEqual((draws + 1)*(success + 1)/(population + 2), d.Mode);
}
public void SetnFails(int n)
{
var d = new Hypergeometric(10, 1, 1);
Assert.Throws <ArgumentOutOfRangeException>(() => d.N = n);
}
public void ValidateProbabilityLn(int population, int success, int draws, int x)
{
var d = new Hypergeometric(population, success, draws);
Assert.AreEqual(Math.Log(d.Probability(x)), d.ProbabilityLn(x));
}
public void ValidateEntropyThrowsNotSupportedException()
{
var d = new Hypergeometric(10, 1, 1);
Assert.Throws <NotSupportedException>(() => { var e = d.Entropy; });
}
public void ValidateToString()
{
var d = new Hypergeometric(10, 1, 1);
Assert.AreEqual("Hypergeometric(N = 10, M = 1, n = 1)", d.ToString());
}
public void ValidateSkewness(int size, int m, int n)
{
var d = new Hypergeometric(size, m, n);
Assert.AreEqual((Math.Sqrt(size - 1.0) * (size - (2 * n)) * (size - (2 * m))) / (Math.Sqrt(n * m * (size - m) * (size - n)) * (size - 2.0)), d.Skewness);
}
public void CanSample()
{
var d = new Hypergeometric(10, 1, 1);
d.Sample();
}
public void ValidateMode(int size, int m, int n)
{
var d = new Hypergeometric(size, m, n);
Assert.AreEqual((n + 1) * (m + 1) / (size + 2), d.Mode);
}
public override void ExecuteExample()
{
// <a href="http://en.wikipedia.org/wiki/Binomial_distribution">Binomial distribution</a>
MathDisplay.WriteLine("<b>Binomial distribution</b>");
// 1. Initialize the new instance of the Binomial distribution class with parameters P = 0.2, N = 20
var binomial = new Binomial(0.2, 20);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Binomial distribution class with parameters P = {0}, N = {1}", binomial.P, binomial.N);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", binomial);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", binomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", binomial.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", binomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", binomial.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", binomial.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", binomial.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", binomial.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", binomial.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", binomial.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", binomial.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", binomial.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", binomial.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Binomial distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Binomial distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(binomial.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Bernoulli_distribution">Bernoulli distribution</a>
MathDisplay.WriteLine("<b>Bernoulli distribution</b>");
// 1. Initialize the new instance of the Bernoulli distribution class with parameter P = 0.2
var bernoulli = new Bernoulli(0.2);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Bernoulli distribution class with parameter P = {0}", bernoulli.P);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", bernoulli);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", bernoulli.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", bernoulli.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", bernoulli.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", bernoulli.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", bernoulli.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", bernoulli.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", bernoulli.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", bernoulli.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", bernoulli.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", bernoulli.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", bernoulli.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Bernoulli distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Bernoulli distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(bernoulli.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Categorical_distribution">Categorical distribution</a>
MathDisplay.WriteLine("<b>Categorical distribution</b>");
// 1. Initialize the new instance of the Categorical distribution class with parameters P = (0.1, 0.2, 0.25, 0.45)
var binomialC = new Categorical(new[] { 0.1, 0.2, 0.25, 0.45 });
MathDisplay.WriteLine(@"1. Initialize the new instance of the Categorical distribution class with parameters P = (0.1, 0.2, 0.25, 0.45)");
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", binomialC);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", binomialC.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", binomialC.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", binomialC.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", binomialC.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", binomialC.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", binomialC.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", binomialC.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", binomialC.Median.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", binomialC.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", binomialC.StdDev.ToString(" #0.00000;-#0.00000"));
// 3. Generate 10 samples of the Categorical distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Categorical distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(binomialC.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Conway%E2%80%93Maxwell%E2%80%93Poisson_distribution">ConwayMaxwellPoisson distribution</a>
MathDisplay.WriteLine("<b>Conway Maxwell Poisson distribution</b>");
// 1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = 2, Nu = 1
var conwayMaxwellPoisson = new ConwayMaxwellPoisson(2, 1);
MathDisplay.WriteLine(@"1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = {0}, Nu = {1}", conwayMaxwellPoisson.Lambda, conwayMaxwellPoisson.Nu);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", conwayMaxwellPoisson);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", conwayMaxwellPoisson.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", conwayMaxwellPoisson.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", conwayMaxwellPoisson.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", conwayMaxwellPoisson.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", conwayMaxwellPoisson.Mean.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", conwayMaxwellPoisson.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", conwayMaxwellPoisson.StdDev.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the ConwayMaxwellPoisson distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the ConwayMaxwellPoisson distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(conwayMaxwellPoisson.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Discrete_uniform">DiscreteUniform distribution</a>
MathDisplay.WriteLine("<b>Discrete Uniform distribution</b>");
// 1. Initialize the new instance of the DiscreteUniform distribution class with parameters LowerBound = 2, UpperBound = 10
var discreteUniform = new DiscreteUniform(2, 10);
MathDisplay.WriteLine(@"1. Initialize the new instance of the DiscreteUniform distribution class with parameters LowerBound = {0}, UpperBound = {1}", discreteUniform.LowerBound, discreteUniform.UpperBound);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", discreteUniform);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", discreteUniform.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", discreteUniform.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", discreteUniform.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", discreteUniform.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", discreteUniform.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", discreteUniform.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", discreteUniform.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", discreteUniform.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", discreteUniform.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", discreteUniform.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", discreteUniform.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", discreteUniform.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the DiscreteUniform distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the DiscreteUniform distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(discreteUniform.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Geometric_distribution">Geometric distribution</a>
MathDisplay.WriteLine("<b>Geometric distribution</b>");
// 1. Initialize the new instance of the Geometric distribution class with parameter P = 0.2
var geometric = new Geometric(0.2);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Geometric distribution class with parameter P = {0}", geometric.P);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", geometric);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", geometric.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", geometric.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", geometric.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", geometric.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", geometric.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", geometric.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", geometric.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", geometric.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", geometric.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", geometric.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", geometric.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", geometric.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Geometric distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Geometric distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(geometric.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Hypergeometric_distribution">Hypergeometric distribution</a>
MathDisplay.WriteLine("<b>Hypergeometric distribution</b>");
// 1. Initialize the new instance of the Hypergeometric distribution class with parameters PopulationSize = 10, M = 2, N = 8
var hypergeometric = new Hypergeometric(30, 15, 10);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Hypergeometric distribution class with parameters Population = {0}, Success = {1}, Draws = {2}", hypergeometric.Population, hypergeometric.Success, hypergeometric.Draws);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", hypergeometric);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", hypergeometric.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", hypergeometric.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", hypergeometric.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", hypergeometric.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", hypergeometric.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", hypergeometric.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", hypergeometric.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", hypergeometric.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", hypergeometric.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", hypergeometric.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Hypergeometric distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Hypergeometric distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(hypergeometric.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Negative_binomial">NegativeBinomial distribution</a>
MathDisplay.WriteLine("<b>Negative Binomial distribution</b>");
// 1. Initialize the new instance of the NegativeBinomial distribution class with parameters P = 0.2, R = 20
var negativeBinomial = new NegativeBinomial(20, 0.2);
MathDisplay.WriteLine(@"1. Initialize the new instance of the NegativeBinomial distribution class with parameters P = {0}, N = {1}", negativeBinomial.P, negativeBinomial.R);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", negativeBinomial);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", negativeBinomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", negativeBinomial.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", negativeBinomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", negativeBinomial.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", negativeBinomial.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", negativeBinomial.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", negativeBinomial.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", negativeBinomial.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", negativeBinomial.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", negativeBinomial.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the NegativeBinomial distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the NegativeBinomial distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(negativeBinomial.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Poisson_distribution">Poisson distribution</a>
MathDisplay.WriteLine("<b>Poisson distribution</b>");
// 1. Initialize the new instance of the Poisson distribution class with parameter Lambda = 1
var poisson = new Poisson(1);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Poisson distribution class with parameter Lambda = {0}", poisson.Lambda);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", poisson);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", poisson.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", poisson.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", poisson.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", poisson.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", poisson.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", poisson.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", poisson.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", poisson.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", poisson.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", poisson.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", poisson.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", poisson.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Poisson distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Poisson distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(poisson.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Zipf_distribution">Zipf distribution</a>
MathDisplay.WriteLine("<b>Zipf distribution</b>");
// 1. Initialize the new instance of the Zipf distribution class with parameters S = 5, N = 10
var zipf = new Zipf(5, 10);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Zipf distribution class with parameters S = {0}, N = {1}", zipf.S, zipf.N);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", zipf);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", zipf.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", zipf.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", zipf.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", zipf.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", zipf.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", zipf.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", zipf.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", zipf.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", zipf.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", zipf.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", zipf.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Zipf distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Zipf distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(zipf.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
}
public void ValidateMedianThrowsNotSupportedException()
{
var d = new Hypergeometric(10, 1, 1);
Assert.Throws <NotSupportedException>(() => { var m = d.Median; });
}
