public void CanCreateConwayMaxwellPoisson(double lambda, double nu)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
Assert.AreEqual(lambda, d.Lambda);
Assert.AreEqual(nu, d.Nu);
}
public ProbabilityOdds MaxWell(double?home, double?away, double theta)
{
var pp = new ConwayMaxwellPoisson((double)home, theta);
var aa = new ConwayMaxwellPoisson((double)away, theta);
var scores = new double[MAX_GOALS, MAX_GOALS];
var scores2 = new double[MAX_GOALS, MAX_GOALS];
// Prever só até 10 golos (0-0, 0-1, .., 0-9)
for (var home_s = 0; home_s < MAX_GOALS; home_s++)
{
for (var away_s = 0; away_s < MAX_GOALS; away_s++)
{
scores2[home_s, away_s] = pp.Probability(home_s) * aa.Probability(away_s) * 100; //Tem aqui o resultado em percentagem
}
}
var under2 = CalculateGoalsUnderOverProbability(scores2, 2.5, "Under");
var over2 = CalculateGoalsUnderOverProbability(scores2, 2.5, "Over");
var _home = CalculateHomeWinProbability(scores2);
var _draw = CalculateDrawProbability(scores2);
var _away = CalculateAwayWinProbability(scores2);
//MessageBox.Show("--MAXWELL--\nHOME: " + _home + "\nAWAY: " + _away + "\nDRAW: " + _draw + "\nUNDER:" + under2 +"\nOVER:" + over2);
return(new ProbabilityOdds {
Home = _home, Away = _away, Draw = _draw, Under25 = under2, Over25 = over2
});
}
public void CanSampleSequence()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var ied = d.Samples();
ied.Take(5).ToArray();
}
public void CanCreateConwayMaxwellPoisson([Values(0.1, 1.0, 2.5, 10.0, Double.PositiveInfinity)] double lambda, [Values(0.0, 2.5, Double.PositiveInfinity)] double nu)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
Assert.AreEqual(lambda, d.Lambda);
Assert.AreEqual(nu, d.Nu);
}
public void ValidateToString()
{
System.Threading.Thread.CurrentThread.CurrentCulture = System.Globalization.CultureInfo.InvariantCulture;
var d = new ConwayMaxwellPoisson(1d, 2d);
Assert.AreEqual("ConwayMaxwellPoisson(λ = 1, ν = 2)", d.ToString());
}
public void ValidateMean(
[Values(1, 2, 10, 20, 1, 2)] int lambda,
[Values(1, 1, 1, 1, 2, 2)] int nu,
[Values(1.0, 2.0, 10.0, 20.0, 0.697774657964008, 1.12635723962342)] double mean)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(mean, d.Mean, 10);
}
public void ValidateCumulativeDistribution(
[Values(1.0, 1.0, 2.0, 2.0, 2.0, 2.0)] double lambda,
[Values(1.0, 1.0, 1.0, 1.0, 2.0, 2.0)] double nu,
[Values(1, 2, 1, 2, 1, 3)] int x,
[Values(0.735758882342885, 0.919698602928606, 0.406005849709838, 0.676676416183064, 0.705492111307356, 0.992914823321464)] double cdf)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(cdf, d.CumulativeDistribution(x), 13);
}
public void ValidateProbability(
[Values(1.0, 1.0, 2.0, 2.0, 2.0, 2.0)] double lambda,
[Values(1.0, 1.0, 1.0, 1.0, 2.0, 2.0)] double nu,
[Values(1, 2, 1, 2, 1, 3)] int x,
[Values(0.367879441171442, 0.183939720585721, 0.270670566473225, 0.270670566473225, 0.470328074204904, 0.052258674911656)] double p)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(p, d.Probability(x), 13);
}
public void ValidateProbabilityLn(
[Values(1.0, 1.0, 2.0, 2.0, 2.0, 2.0)] double lambda,
[Values(1.0, 1.0, 1.0, 1.0, 2.0, 2.0)] double nu,
[Values(1, 2, 1, 2, 1, 3)] int x,
[Values(-1.0, -1.69314718055995, -1.30685281944005, -1.30685281944005, -0.754324797564617, -2.95154937490084)] double pln)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(pln, d.ProbabilityLn(x), 13);
}
public void ValidateMaximumThrowsNotSupportedException()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws<NotSupportedException>(() => { var max = d.Maximum; });
}
public void ValidateMean(int lambda, int nu, double mean)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqualRelative(mean, d.Mean, 10);
}
public void ValidateEntropyThrowsNotSupportedException()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws<NotSupportedException>(() => { var e = d.Entropy; });
}
public void ValidateCumulativeDistribution(double lambda, double nu, int x, double cdf)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(cdf, d.CumulativeDistribution(x), 13);
}
public void ValidateMaximumThrowsNotSupportedException()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws <NotSupportedException>(() => { var max = d.Maximum; });
}
public void ValidateEntropyThrowsNotSupportedException()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws <NotSupportedException>(() => { var e = d.Entropy; });
}
public void ValidateToString()
{
var d = new ConwayMaxwellPoisson(1d, 2d);
Assert.AreEqual("ConwayMaxwellPoisson(λ = 1, ν = 2)", d.ToString());
}
public void ValidateToString()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.AreEqual <string>("ConwayMaxwellPoisson(Lambda = 1, Nu = 2)", d.ToString());
}
public void ConwayMaxwellPoissonCreateFailsWithBadParameters(double lambda, double nu)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
}
public void ValidateMaximum()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var max = d.Maximum;
}
public void ValidateMedian()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var m = d.Median;
}
public void ValidateMode()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var m = d.Mode;
}
public void ValidateSkewness()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var s = d.Skewness;
}
public void CanCreateConwayMaxwellPoisson([Values(0.1, 1.0, 2.5, 10.0, Double.PositiveInfinity)] double lambda, [Values(0.0, 2.5, Double.PositiveInfinity)] double nu)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
Assert.AreEqual(lambda, d.Lambda);
Assert.AreEqual(nu, d.Nu);
}
public void CanSample()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
d.Sample();
}
public void ValidateCumulativeDistribution(double lambda, double nu, int x, double cdf)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqualRelative(cdf, d.CumulativeDistribution(x), 12);
}
public void CanSetLambda(double lambda)
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
d.Lambda = lambda;
}
public void SetLambdaFails(double lambda)
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws <ArgumentOutOfRangeException>(() => d.Lambda = lambda);
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Conway%E2%80%93Maxwell%E2%80%93Poisson_distribution">ConwayMaxwellPoisson distribution</a>
public void Run()
{
// 1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = 2, Nu = 1
var binomial = new ConwayMaxwellPoisson(2, 1);
Console.WriteLine(@"1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = {0}, Nu = {1}", binomial.Lambda, binomial.Nu);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", binomial);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '3'", binomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability mass at location '3'", binomial.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability mass at location '3'", binomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", binomial.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", binomial.Mean.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", binomial.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", binomial.StdDev.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the ConwayMaxwellPoisson distribution
Console.WriteLine(@"3. Generate 10 samples of the ConwayMaxwellPoisson distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(binomial.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the ConwayMaxwellPoisson(4, 1) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the ConwayMaxwellPoisson(4, 1) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = binomial.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the ConwayMaxwellPoisson(2, 1) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the ConwayMaxwellPoisson(2, 1) distribution and display histogram");
binomial.Lambda = 2;
for (var i = 0; i < data.Length; i++)
{
data[i] = binomial.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the ConwayMaxwellPoisson(5, 2) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the ConwayMaxwellPoisson(5, 2) distribution and display histogram");
binomial.Lambda = 5;
binomial.Nu = 2;
for (var i = 0; i < data.Length; i++)
{
data[i] = binomial.Sample();
}
ConsoleHelper.DisplayHistogram(data);
}
public void ValidateMean(int lambda, int nu, double mean)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(mean, d.Mean, 10);
}
public void ConwayMaxwellPoissonCreateFailsWithBadParameters(double lambda, double nu)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
}
public void CanSample()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
d.Sample();
}
public void ValidateEntropy()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var e = d.Entropy;
}
public void ValidateProbabilityLn(
[Values(1.0, 1.0, 2.0, 2.0, 2.0, 2.0)] double lambda,
[Values(1.0, 1.0, 1.0, 1.0, 2.0, 2.0)] double nu,
[Values(1, 2, 1, 2, 1, 3)] int x,
[Values(-1.0, -1.69314718055995, -1.30685281944005, -1.30685281944005, -0.754324797564617, -2.95154937490084)] double pln)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(pln, d.ProbabilityLn(x), 13);
}
public void ValidateMaximum()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var max = d.Maximum;
}
public void ValidateSkewnessThrowsNotSupportedException()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws<NotSupportedException>(() => { var s = d.Skewness; });
}
public void ValidateMedian()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var m = d.Median;
}
public void ValidateMinimum()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.AreEqual(0.0, d.Minimum);
}
public void ValidateMode()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var m = d.Mode;
}
public void ValidateProbabilityLn(double lambda, double nu, int x, double pln)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqualRelative(pln, d.ProbabilityLn(x), 12);
}
public void ValidateProbability(double lambda, double nu, int x, double p)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(p, d.Probability(x), 13);
}
public void CanSampleSequence()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var ied = d.Samples();
ied.Take(5).ToArray();
}
public void ValidateSkewness()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
var s = d.Skewness;
}
public void CanCreateConwayMaxwellPoisson(double lambda, double nu)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
Assert.AreEqual(lambda, d.Lambda);
Assert.AreEqual(nu, d.Nu);
}
public void ValidateToString()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.AreEqual<string>("ConwayMaxwellPoisson(Lambda = 1, Nu = 2)", d.ToString());
}
public void ValidateProbability(double lambda, double nu, int x, double p)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqualRelative(p, d.Probability(x), 12);
}
public void CanSetLambda(double lambda)
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
d.Lambda = lambda;
}
public void SetNuFails(double nu)
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws <ArgumentOutOfRangeException>(() => d.Nu = nu);
}
public void CanSetNu(double nu)
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
d.Nu = nu;
}
public void ValidateSkewnessThrowsNotSupportedException()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws <NotSupportedException>(() => { var s = d.Skewness; });
}
public void ValidateToString()
{
var d = new ConwayMaxwellPoisson(1d, 2d);
Assert.AreEqual("ConwayMaxwellPoisson(λ = 1, ν = 2)", d.ToString());
}
public void ValidateMinimum()
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.AreEqual(0.0, d.Minimum);
}
public void ValidateMean(
[Values(1, 2, 10, 20, 1, 2)] int lambda,
[Values(1, 1, 1, 1, 2, 2)] int nu,
[Values(1.0, 2.0, 10.0, 20.0, 0.697774657964008, 1.12635723962342)] double mean)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(mean, d.Mean, 10);
}
public void ValidateProbabilityLn(double lambda, double nu, int x, double pln)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(pln, d.ProbabilityLn(x), 13);
}
public void SetLambdaFails(double lambda)
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws<ArgumentOutOfRangeException>(() => d.Lambda = lambda);
}
public void ValidateCumulativeDistribution(
[Values(1.0, 1.0, 2.0, 2.0, 2.0, 2.0)] double lambda,
[Values(1.0, 1.0, 1.0, 1.0, 2.0, 2.0)] double nu,
[Values(1, 2, 1, 2, 1, 3)] int x,
[Values(0.735758882342885, 0.919698602928606, 0.406005849709838, 0.676676416183064, 0.705492111307356, 0.992914823321464)] double cdf)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(cdf, d.CumulativeDistribution(x), 13);
}
public void SetNuFails(double nu)
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.Throws<ArgumentOutOfRangeException>(() => d.Nu = nu);
}
public void SetLambdaFails(double lambda)
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.That(() => d.Lambda = lambda, Throws.ArgumentException);
}
public void ValidateProbability(
[Values(1.0, 1.0, 2.0, 2.0, 2.0, 2.0)] double lambda,
[Values(1.0, 1.0, 1.0, 1.0, 2.0, 2.0)] double nu,
[Values(1, 2, 1, 2, 1, 3)] int x,
[Values(0.367879441171442, 0.183939720585721, 0.270670566473225, 0.270670566473225, 0.470328074204904, 0.052258674911656)] double p)
{
var d = new ConwayMaxwellPoisson(lambda, nu);
AssertHelpers.AlmostEqual(p, d.Probability(x), 13);
}
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 SetNuFails(double nu)
{
var d = new ConwayMaxwellPoisson(1.0, 2.0);
Assert.That(() => d.Nu = nu, Throws.ArgumentException);
}
