public void FailSampleStatic() { Assert.Throws <ArgumentOutOfRangeException>(() => ChiSquare.Sample(new Random(), -1.0)); }
public void CanSample() { var n = new ChiSquare(1.0); n.Sample(); }
public void CanSampleStatic() { ChiSquare.Sample(new Random(), 2.0); }
/// <summary> /// Run example /// </summary> /// <a href="http://en.wikipedia.org/wiki/Chi-square_distribution">ChiSquare distribution</a> public void Run() { // 1. Initialize the new instance of the ChiSquare distribution class with parameter dof = 1. var chiSquare = new ChiSquare(1); Console.WriteLine(@"1. Initialize the new instance of the ChiSquare distribution class with parameter DegreesOfFreedom = {0}", chiSquare.DegreesOfFreedom); Console.WriteLine(); // 2. Distributuion properties: Console.WriteLine(@"2. {0} distributuion properties:", chiSquare); // Cumulative distribution function Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", chiSquare.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000")); // Probability density Console.WriteLine(@"{0} - Probability density at location '0.3'", chiSquare.Density(0.3).ToString(" #0.00000;-#0.00000")); // Log probability density Console.WriteLine(@"{0} - Log probability density at location '0.3'", chiSquare.DensityLn(0.3).ToString(" #0.00000;-#0.00000")); // Entropy Console.WriteLine(@"{0} - Entropy", chiSquare.Entropy.ToString(" #0.00000;-#0.00000")); // Largest element in the domain Console.WriteLine(@"{0} - Largest element in the domain", chiSquare.Maximum.ToString(" #0.00000;-#0.00000")); // Smallest element in the domain Console.WriteLine(@"{0} - Smallest element in the domain", chiSquare.Minimum.ToString(" #0.00000;-#0.00000")); // Mean Console.WriteLine(@"{0} - Mean", chiSquare.Mean.ToString(" #0.00000;-#0.00000")); // Median Console.WriteLine(@"{0} - Median", chiSquare.Median.ToString(" #0.00000;-#0.00000")); // Mode Console.WriteLine(@"{0} - Mode", chiSquare.Mode.ToString(" #0.00000;-#0.00000")); // Variance Console.WriteLine(@"{0} - Variance", chiSquare.Variance.ToString(" #0.00000;-#0.00000")); // Standard deviation Console.WriteLine(@"{0} - Standard deviation", chiSquare.StdDev.ToString(" #0.00000;-#0.00000")); // Skewness Console.WriteLine(@"{0} - Skewness", chiSquare.Skewness.ToString(" #0.00000;-#0.00000")); Console.WriteLine(); // 3. Generate 10 samples of the ChiSquare distribution Console.WriteLine(@"3. Generate 10 samples of the ChiSquare distribution"); for (var i = 0; i < 10; i++) { Console.Write(chiSquare.Sample().ToString("N05") + @" "); } Console.WriteLine(); Console.WriteLine(); // 4. Generate 100000 samples of the ChiSquare(1) distribution and display histogram Console.WriteLine(@"4. Generate 100000 samples of the ChiSquare(1) distribution and display histogram"); var data = new double[100000]; for (var i = 0; i < data.Length; i++) { data[i] = chiSquare.Sample(); } ConsoleHelper.DisplayHistogram(data); Console.WriteLine(); // 5. Generate 100000 samples of the ChiSquare(4) distribution and display histogram Console.WriteLine(@"5. Generate 100000 samples of the ChiSquare(4) distribution and display histogram"); chiSquare.DegreesOfFreedom = 4; for (var i = 0; i < data.Length; i++) { data[i] = chiSquare.Sample(); } ConsoleHelper.DisplayHistogram(data); Console.WriteLine(); // 6. Generate 100000 samples of the ChiSquare(8) distribution and display histogram Console.WriteLine(@"6. Generate 100000 samples of the ChiSquare(8) distribution and display histogram"); chiSquare.DegreesOfFreedom = 8; for (var i = 0; i < data.Length; i++) { data[i] = chiSquare.Sample(); } ConsoleHelper.DisplayHistogram(data); }
public void FailSampleStatic() { var d = ChiSquare.Sample(new Random(), -1.0); }
/// <summary> /// Run example /// </summary> /// <seealso cref="http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient">Pearson product-moment correlation coefficient</seealso> public void Run() { // 1. Initialize the new instance of the ChiSquare distribution class with parameter dof = 5. var chiSquare = new ChiSquare(5); Console.WriteLine(@"1. Initialize the new instance of the ChiSquare distribution class with parameter DegreesOfFreedom = {0}", chiSquare.DegreesOfFreedom); Console.WriteLine(@"{0} distributuion properties:", chiSquare); Console.WriteLine(@"{0} - Largest element", chiSquare.Maximum.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Smallest element", chiSquare.Minimum.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Mean", chiSquare.Mean.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Median", chiSquare.Median.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Mode", chiSquare.Mode.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Variance", chiSquare.Variance.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Standard deviation", chiSquare.StdDev.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Skewness", chiSquare.Skewness.ToString(" #0.00000;-#0.00000")); Console.WriteLine(); // 2. Generate 1000 samples of the ChiSquare(5) distribution Console.WriteLine(@"2. Generate 1000 samples of the ChiSquare(5) distribution"); var data = new double[1000]; for (var i = 0; i < data.Length; i++) { data[i] = chiSquare.Sample(); } // 3. Get basic statistics on set of generated data using extention methods Console.WriteLine(@"3. Get basic statistics on set of generated data using extention methods"); Console.WriteLine(@"{0} - Largest element", data.Maximum().ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Smallest element", data.Minimum().ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Mean", data.Mean().ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Median", data.Median().ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Biased population variance", data.PopulationVariance().ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Variance", data.Variance().ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Standard deviation", data.StandardDeviation().ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Biased sample standard deviation", data.PopulationStandardDeviation().ToString(" #0.00000;-#0.00000")); Console.WriteLine(); // 4. Compute the basic statistics of data set using DescriptiveStatistics class Console.WriteLine(@"4. Compute the basic statistics of data set using DescriptiveStatistics class"); var descriptiveStatistics = new DescriptiveStatistics(data); Console.WriteLine(@"{0} - Kurtosis", descriptiveStatistics.Kurtosis.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Largest element", descriptiveStatistics.Maximum.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Smallest element", descriptiveStatistics.Minimum.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Mean", descriptiveStatistics.Mean.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Variance", descriptiveStatistics.Variance.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Standard deviation", descriptiveStatistics.StandardDeviation.ToString(" #0.00000;-#0.00000")); Console.WriteLine(@"{0} - Skewness", descriptiveStatistics.Skewness.ToString(" #0.00000;-#0.00000")); Console.WriteLine(); // Generate 1000 samples of the ChiSquare(2.5) distribution var chiSquareB = new ChiSquare(2); var dataB = new double[1000]; for (var i = 0; i < data.Length; i++) { dataB[i] = chiSquareB.Sample(); } // 5. Correlation coefficient between 1000 samples of ChiSquare(5) and ChiSquare(2.5) Console.WriteLine(@"5. Correlation coefficient between 1000 samples of ChiSquare(5) and ChiSquare(2.5) is {0}", Correlation.Pearson(data, dataB).ToString("N04")); Console.WriteLine(); // 6. Correlation coefficient between 1000 samples of f(x) = x * 2 and f(x) = x * x data = SignalGenerator.EquidistantInterval(x => x * 2, 0, 100, 1000); dataB = SignalGenerator.EquidistantInterval(x => x * x, 0, 100, 1000); Console.WriteLine(@"6. Correlation coefficient between 1000 samples of f(x) = x * 2 and f(x) = x * x is {0}", Correlation.Pearson(data, dataB).ToString("N04")); Console.WriteLine(); }