public void GcdLcmTest()
{
long a = 3457832408;
long b = 56789309233;
Assert.IsTrue(AdvancedIntegerMath.GCF(a, b) * AdvancedIntegerMath.LCM(a, b) == a * b);
}
public void TwoSampleKolmogorovNullDistributionTest()
{
ContinuousDistribution population = new ExponentialDistribution();
int[] sizes = new int[] { 23, 30, 175 };
foreach (int na in sizes)
{
foreach (int nb in sizes)
{
Console.WriteLine("{0} {1}", na, nb);
Sample d = new Sample();
ContinuousDistribution nullDistribution = null;
for (int i = 0; i < 128; i++)
{
Sample a = TestUtilities.CreateSample(population, na, 31415 + na + i);
Sample b = TestUtilities.CreateSample(population, nb, 27182 + nb + i);
TestResult r = Sample.KolmogorovSmirnovTest(a, b);
d.Add(r.Statistic);
nullDistribution = r.Distribution;
}
// Only do full KS test if the number of bins is larger than the sample size, otherwise we are going to fail
// because the KS test detects the granularity of the distribution
TestResult mr = d.KolmogorovSmirnovTest(nullDistribution);
Console.WriteLine(mr.LeftProbability);
if (AdvancedIntegerMath.LCM(na, nb) > d.Count)
{
Assert.IsTrue(mr.LeftProbability < 0.99);
}
// But always test that mean and standard deviation are as expected
Console.WriteLine("{0} {1}", nullDistribution.Mean, d.PopulationMean.ConfidenceInterval(0.99));
Assert.IsTrue(d.PopulationMean.ConfidenceInterval(0.99).ClosedContains(nullDistribution.Mean));
Console.WriteLine("{0} {1}", nullDistribution.StandardDeviation, d.PopulationStandardDeviation.ConfidenceInterval(0.99));
Assert.IsTrue(d.PopulationStandardDeviation.ConfidenceInterval(0.99).ClosedContains(nullDistribution.StandardDeviation));
Console.WriteLine("{0} {1}", nullDistribution.CentralMoment(3), d.PopulationCentralMoment(3).ConfidenceInterval(0.99));
//Assert.IsTrue(d.PopulationMomentAboutMean(3).ConfidenceInterval(0.99).ClosedContains(nullDistribution.MomentAboutMean(3)));
//Console.WriteLine("m {0} {1}", nullDistribution.Mean, d.PopulationMean);
}
}
}
public void TwoSampleKolmogorovNullDistributionTest()
{
Random rng = new Random(4);
ContinuousDistribution population = new ExponentialDistribution();
int[] sizes = new int[] { 23, 30, 175 };
foreach (int na in sizes)
{
foreach (int nb in sizes)
{
Sample d = new Sample();
ContinuousDistribution nullDistribution = null;
for (int i = 0; i < 128; i++)
{
List <double> a = TestUtilities.CreateDataSample(rng, population, na).ToList();
List <double> b = TestUtilities.CreateDataSample(rng, population, nb).ToList();
TestResult r = Univariate.KolmogorovSmirnovTest(a, b);
d.Add(r.Statistic.Value);
nullDistribution = r.Statistic.Distribution;
}
// Only do full KS test if the number of bins is larger than the sample size, otherwise we are going to fail
// because the KS test detects the granularity of the distribution.
TestResult mr = d.KolmogorovSmirnovTest(nullDistribution);
if (AdvancedIntegerMath.LCM(na, nb) > d.Count)
{
Assert.IsTrue(mr.Probability > 0.01);
}
// But always test that mean and standard deviation are as expected
Assert.IsTrue(d.PopulationMean.ConfidenceInterval(0.99).ClosedContains(nullDistribution.Mean));
Assert.IsTrue(d.PopulationStandardDeviation.ConfidenceInterval(0.99).ClosedContains(nullDistribution.StandardDeviation));
// This test is actually a bit sensitive, probably because the discrete-ness of the underlying distribution
// and the inaccuracy of the asymptotic approximation for intermediate sample size make strict comparisons iffy.
}
}
}