public void ValidateDensity([Values(0.0, 0.0, 0.0, 0.0, -5.0, 0.0)] double lower, [Values(0.0, 0.1, 1.0, 10.0, 100.0, Double.PositiveInfinity)] double upper) { var n = new ContinuousUniform(lower, upper); for (var i = 0; i < 11; i++) { var x = i - 5.0; if (x >= lower && x <= upper) { Assert.AreEqual(1.0 / (upper - lower), n.Density(x)); } else { Assert.AreEqual(0.0, n.Density(x)); } } }
public void ValidateDensity(double lower, double upper) { var n = new ContinuousUniform(lower, upper); for (var i = 0; i < 11; i++) { var x = i - 5.0; if (x >= lower && x <= upper) { Assert.AreEqual(1.0 / (upper - lower), n.Density(x)); } else { Assert.AreEqual(0.0, n.Density(x)); } } }
/// <summary> /// Run example /// </summary> /// <a href="http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29">ContinuousUniform distribution</a> public void Run() { // 1. Initialize the new instance of the ContinuousUniform distribution class with default parameters. var continuousUniform = new ContinuousUniform(); Console.WriteLine(@"1. Initialize the new instance of the ContinuousUniform distribution class with parameters Lower = {0}, Upper = {1}", continuousUniform.Lower, continuousUniform.Upper); Console.WriteLine(); // 2. Distributuion properties: Console.WriteLine(@"2. {0} distributuion properties:", continuousUniform); // Cumulative distribution function Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", continuousUniform.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000")); // Probability density Console.WriteLine(@"{0} - Probability density at location '0.3'", continuousUniform.Density(0.3).ToString(" #0.00000;-#0.00000")); // Log probability density Console.WriteLine(@"{0} - Log probability density at location '0.3'", continuousUniform.DensityLn(0.3).ToString(" #0.00000;-#0.00000")); // Entropy Console.WriteLine(@"{0} - Entropy", continuousUniform.Entropy.ToString(" #0.00000;-#0.00000")); // Largest element in the domain Console.WriteLine(@"{0} - Largest element in the domain", continuousUniform.Maximum.ToString(" #0.00000;-#0.00000")); // Smallest element in the domain Console.WriteLine(@"{0} - Smallest element in the domain", continuousUniform.Minimum.ToString(" #0.00000;-#0.00000")); // Mean Console.WriteLine(@"{0} - Mean", continuousUniform.Mean.ToString(" #0.00000;-#0.00000")); // Median Console.WriteLine(@"{0} - Median", continuousUniform.Median.ToString(" #0.00000;-#0.00000")); // Mode Console.WriteLine(@"{0} - Mode", continuousUniform.Mode.ToString(" #0.00000;-#0.00000")); // Variance Console.WriteLine(@"{0} - Variance", continuousUniform.Variance.ToString(" #0.00000;-#0.00000")); // Standard deviation Console.WriteLine(@"{0} - Standard deviation", continuousUniform.StdDev.ToString(" #0.00000;-#0.00000")); // Skewness Console.WriteLine(@"{0} - Skewness", continuousUniform.Skewness.ToString(" #0.00000;-#0.00000")); Console.WriteLine(); // 3. Generate 10 samples of the ContinuousUniform distribution Console.WriteLine(@"3. Generate 10 samples of the ContinuousUniform distribution"); for (var i = 0; i < 10; i++) { Console.Write(continuousUniform.Sample().ToString("N05") + @" "); } Console.WriteLine(); Console.WriteLine(); // 4. Generate 100000 samples of the ContinuousUniform(0, 1) distribution and display histogram Console.WriteLine(@"4. Generate 100000 samples of the ContinuousUniform(0, 1) distribution and display histogram"); var data = new double[100000]; for (var i = 0; i < data.Length; i++) { data[i] = continuousUniform.Sample(); } ConsoleHelper.DisplayHistogram(data); Console.WriteLine(); // 5. Generate 100000 samples of the ContinuousUniform(2, 10) distribution and display histogram Console.WriteLine(@"5. Generate 100000 samples of the ContinuousUniform(2, 10) distribution and display histogram"); continuousUniform.Upper = 10; continuousUniform.Lower = 2; for (var i = 0; i < data.Length; i++) { data[i] = continuousUniform.Sample(); } ConsoleHelper.DisplayHistogram(data); }
public void ValidateDensity(double lower, double upper) { var n = new ContinuousUniform(lower, upper); for (int i = 0; i < 11; i++) { double x = i - 5.0; if(x >= lower && x <= upper) { Assert.AreEqual<double>(1.0 / (upper - lower), n.Density(x)); } else { Assert.AreEqual<double>(0.0, n.Density(x)); } } }
static void Main(string[] args) { // Binomial var p = 0.174; var n = 6; var x = 5; var bCdf = Binomial.CDF(p, n, x); var bPmf = Binomial.PMF(p, n, x); var bPmfLn = Binomial.PMFLn(p, n, x); var b = new Binomial(p, n); var _bCdf = b.CumulativeDistribution(x); var _bPmf = b.Probability(x); var _bPmfLn = b.ProbabilityLn(x); // Normal var mean = 1012.5; var stdDev = 24.8069; var x1 = 1000; var x2 = 1025; var nCdf = Normal.CDF(mean, stdDev, x2) - Normal.CDF(mean, stdDev, x1); var nPdf = Normal.PDF(mean, stdDev, x2) - Normal.PDF(mean, stdDev, x1); var nPdfLn = Normal.PDFLn(mean, stdDev, x2) - Normal.PDFLn(mean, stdDev, x1); var resN = new List <double>(); for (int i = 950; i < 1075; i++) { //resN.Add(Normal.CDF(mean, stdDev, 1075) - Normal.CDF(mean, stdDev, i)); resN.Add(Normal.CDF(mean, stdDev, i)); } var _n = new Normal(mean, stdDev); var _nCdf = b.CumulativeDistribution(x2) - b.CumulativeDistribution(x1); var _nPdf = b.Probability(x2) - b.Probability(x1); var _nPdfLn = b.ProbabilityLn(x2) - b.ProbabilityLn(x1); var nSamples = new double[100]; _n.Samples(nSamples); // Discrete Uniform var sqrtThree = Math.Sqrt(3); var _mean = 2.1; var _stdDev = 1.465; var lower = (_mean - sqrtThree * _stdDev); var upper = (_mean + sqrtThree * _stdDev); var u = new ContinuousUniform(lower, upper); var _p = u.Density(1.05); var h = new Histogram(nSamples, 5); Regex _regexBack = new Regex(@"(?:(?<Lower>[\d]+)? *-? *(?:(?<Upper>[\d]+)))"); var test0 = "66"; var select = test0; if (_regexBack.IsMatch(select)) { foreach (var match in _regexBack.Matches(select)) { } } Console.ReadKey(); }