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); }