public void SampleFitChiSquaredTest()
{
Distribution distribution = new ChiSquaredDistribution(4);
Sample sample = CreateSample(distribution, 100);
// fit to normal should be bad
// this is harder than others, because a chi^2 isn't so very different from a normal; to help, increse N or decrease vu
FitResult nfit = NormalDistribution.FitToSample(sample);
Console.WriteLine("P_n = {0}", nfit.GoodnessOfFit.LeftProbability);
Assert.IsTrue(nfit.GoodnessOfFit.LeftProbability > 0.95, String.Format("P_n = {0}", nfit.GoodnessOfFit.LeftProbability));
// fit to exponential should also be bad
FitResult efit = ExponentialDistribution.FitToSample(sample);
Console.WriteLine("P_e = {0}", efit.GoodnessOfFit.LeftProbability);
Assert.IsTrue(efit.GoodnessOfFit.LeftProbability > 0.95, String.Format("P_e = {0}", efit.GoodnessOfFit.LeftProbability));
}
public void FisherTest()
{
// we will need a RNG
Random rng = new Random(314159);
int n1 = 1;
int n2 = 2;
// define chi squared distributions
Distribution d1 = new ChiSquaredDistribution(n1);
Distribution d2 = new ChiSquaredDistribution(n2);
// create a sample of chi-squared variates
Sample s = new Sample();
for (int i = 0; i < 250; i++) {
double x1 = d1.InverseLeftProbability(rng.NextDouble());
double x2 = d2.InverseLeftProbability(rng.NextDouble());
double x = (x1/n1) / (x2/n2);
s.Add(x);
}
// it should match a Fisher distribution with the appropriate parameters
Distribution f0 = new FisherDistribution(n1, n2);
TestResult t0 = s.KuiperTest(f0);
Console.WriteLine(t0.LeftProbability);
Assert.IsTrue(t0.LeftProbability < 0.95);
// it should be distinguished from a Fisher distribution with different parameters
Distribution f1 = new FisherDistribution(n1 + 1, n2);
TestResult t1 = s.KuiperTest(f1);
Console.WriteLine(t1.LeftProbability);
Assert.IsTrue(t1.LeftProbability > 0.95);
}
public void Bug2811()
{
ChiSquaredDistribution d = new ChiSquaredDistribution(1798);
double x = d.InverseLeftProbability(0.975);
Console.WriteLine(x);
}
