public void ValidateCumulativeDistribution(double lambda, double x) { var n = new Exponential(lambda); if (x >= 0.0) { Assert.AreEqual <double>(1.0 - Math.Exp(-lambda * x), n.CumulativeDistribution(x)); } else { Assert.AreEqual <double>(0.0, n.CumulativeDistribution(x)); } }
public void ValidateCumulativeDistribution( [Values(0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double lambda, [Values(0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double x) { var n = new Exponential(lambda); if (x >= 0.0) { Assert.AreEqual(1.0 - Math.Exp(-lambda * x), n.CumulativeDistribution(x)); } else { Assert.AreEqual(0.0, n.CumulativeDistribution(x)); } }
public double CumulativeProbability(Position1 sample) { if (sample <= Domain.Entry) { return(0); } else if (sample <= Domain.Exit) { return(distribution.CumulativeDistribution(((float)sample).Previous() - Domain.Entry)); } else { return(distribution.CumulativeDistribution(Domain.CoveredArea)); } }
/// <summary> /// Run example /// </summary> /// <a href="http://en.wikipedia.org/wiki/Exponential_distribution">Exponential distribution</a> public void Run() { // 1. Initialize the new instance of the Exponential distribution class with parameter Lambda = 1. var exponential = new Exponential(1); Console.WriteLine(@"1. Initialize the new instance of the Exponential distribution class with parameter Lambda = {0}", exponential.Rate); Console.WriteLine(); // 2. Distributuion properties: Console.WriteLine(@"2. {0} distributuion properties:", exponential); // Cumulative distribution function Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", exponential.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000")); // Probability density Console.WriteLine(@"{0} - Probability density at location '0.3'", exponential.Density(0.3).ToString(" #0.00000;-#0.00000")); // Log probability density Console.WriteLine(@"{0} - Log probability density at location '0.3'", exponential.DensityLn(0.3).ToString(" #0.00000;-#0.00000")); // Entropy Console.WriteLine(@"{0} - Entropy", exponential.Entropy.ToString(" #0.00000;-#0.00000")); // Largest element in the domain Console.WriteLine(@"{0} - Largest element in the domain", exponential.Maximum.ToString(" #0.00000;-#0.00000")); // Smallest element in the domain Console.WriteLine(@"{0} - Smallest element in the domain", exponential.Minimum.ToString(" #0.00000;-#0.00000")); // Mean Console.WriteLine(@"{0} - Mean", exponential.Mean.ToString(" #0.00000;-#0.00000")); // Median Console.WriteLine(@"{0} - Median", exponential.Median.ToString(" #0.00000;-#0.00000")); // Mode Console.WriteLine(@"{0} - Mode", exponential.Mode.ToString(" #0.00000;-#0.00000")); // Variance Console.WriteLine(@"{0} - Variance", exponential.Variance.ToString(" #0.00000;-#0.00000")); // Standard deviation Console.WriteLine(@"{0} - Standard deviation", exponential.StdDev.ToString(" #0.00000;-#0.00000")); // Skewness Console.WriteLine(@"{0} - Skewness", exponential.Skewness.ToString(" #0.00000;-#0.00000")); Console.WriteLine(); // 3. Generate 10 samples of the Exponential distribution Console.WriteLine(@"3. Generate 10 samples of the Exponential distribution"); for (var i = 0; i < 10; i++) { Console.Write(exponential.Sample().ToString("N05") + @" "); } Console.WriteLine(); Console.WriteLine(); // 4. Generate 100000 samples of the Exponential(1) distribution and display histogram Console.WriteLine(@"4. Generate 100000 samples of the Exponential(1) distribution and display histogram"); var data = new double[100000]; for (var i = 0; i < data.Length; i++) { data[i] = exponential.Sample(); } ConsoleHelper.DisplayHistogram(data); Console.WriteLine(); // 5. Generate 100000 samples of the Exponential(9) distribution and display histogram Console.WriteLine(@"5. Generate 100000 samples of the Exponential(9) distribution and display histogram"); exponential.Rate = 9; for (var i = 0; i < data.Length; i++) { data[i] = exponential.Sample(); } ConsoleHelper.DisplayHistogram(data); Console.WriteLine(); // 6. Generate 100000 samples of the Exponential(0.01) distribution and display histogram Console.WriteLine(@"6. Generate 100000 samples of the Exponential(0.01) distribution and display histogram"); exponential.Rate = 0.01; for (var i = 0; i < data.Length; i++) { data[i] = exponential.Sample(); } ConsoleHelper.DisplayHistogram(data); }
public void ValidateCumulativeDistribution(double lambda, double x) { var n = new Exponential(lambda); if (x >= 0.0) { Assert.AreEqual(1.0 - Math.Exp(-lambda * x), n.CumulativeDistribution(x)); } else { Assert.AreEqual(0.0, n.CumulativeDistribution(x)); } }
private void obtenerFrecuenciasEsperadas() { double media = MathNet.Numerics.Statistics.ArrayStatistics.Mean(datos.ToArray()); switch (distribucionElegida) { case TipoDistribucion.continuaExponencial: /* * En el caso de haber elegido la distribucion exponencial, tenemos que: * * lambda(media) = 1 / (media muestral); * */ //obtenemos el lambda para esta distribucion double lambda = 1 / media; //generamos la distribucion para el lambda dado: Exponential exponencial = new Exponential(lambda); //recorremos los intervalos para obtener los valores minimos y maximos, y asi calcular la frecuencia esperada foreach (Intervalo intervalo in intervalos) { intervalo.frecuenciaEsperada = (exponencial.CumulativeDistribution(intervalo.limiteSuperior) - exponencial.CumulativeDistribution(intervalo.limiteInferior)) * datos.Count(); intervalo.frecuenciaEsperada = Math.Round(intervalo.frecuenciaEsperada, CANTIDAD_DECIMALES); } break; case TipoDistribucion.continuaUniforme: /* * En el caso de haber elegido uniforme, tenemos que: * * FE = cantidad de datos de la muestra / cantidad de intervalos; * */ //Entonces obtenemos este dato y se lo asignamos a todos los intervalos. double frecuenciaEsperada = (double)datos.Count() / (double)intervalos.Count(); foreach (Intervalo intervalo in intervalos) { intervalo.frecuenciaEsperada = frecuenciaEsperada; intervalo.frecuenciaEsperada = Math.Round(intervalo.frecuenciaEsperada, CANTIDAD_DECIMALES); } break; case TipoDistribucion.continuaNormal: /* * Para distribucion normal tenemos: * * desviacion estandar (sigma) = raiz cuadrada de la media; * */ //obtenemos la varianza double varianza = MathNet.Numerics.Statistics.ArrayStatistics.Variance(datos.ToArray()); //calculamos la deviacion estandar double desviacionEstandar = Math.Sqrt(varianza); //creo la distribucion normal MathNet.Numerics.Distributions.Normal normal = new MathNet.Numerics.Distributions.Normal(media, desviacionEstandar); foreach (Intervalo intervalo in intervalos) { intervalo.frecuenciaEsperada = (normal.CumulativeDistribution(intervalo.limiteSuperior) - normal.CumulativeDistribution(intervalo.limiteInferior)) * datos.Count(); intervalo.frecuenciaEsperada = Math.Round(intervalo.frecuenciaEsperada, CANTIDAD_DECIMALES); } break; case TipoDistribucion.continuaPoisson: double lambdaPoisson = 1 / media; Poisson poisson = new Poisson(media); //recorremos los intervalos para obtener los valores minimos y maximos, y asi calcular la frecuencia esperada foreach (Intervalo intervalo in intervalos) { intervalo.frecuenciaEsperada = (poisson.CumulativeDistribution(intervalo.limiteInferior) - poisson.CumulativeDistribution(intervalo.limiteInferior - 1)) * datos.Count(); intervalo.frecuenciaEsperada = Math.Round(intervalo.frecuenciaEsperada, CANTIDAD_DECIMALES); } break; } }