internal double Probability(TestType type)
{
switch (type)
{
case TestType.LeftTailed:
return(distribution.LeftProbability(value));
case TestType.RightTailed:
return(distribution.RightProbability(value));
case TestType.TwoTailed:
// This certainly works for symmetric distributions.
// It also works for F-test, which is the one asymmetric case we care about
// for the distributions we cover. Not clear that it is true in general, though.
if (value < distribution.Median)
{
return(2.0 * distribution.LeftProbability(value));
}
else
{
return(2.0 * distribution.RightProbability(value));
}
default:
throw new InvalidOperationException();
}
}
private void DistributionProbabilityTestHelper(ContinuousDistribution distribution, double x)
{
Console.WriteLine("{0} {1}", distribution.GetType().Name, x);
double P = distribution.LeftProbability(x);
double Q = distribution.RightProbability(x);
Assert.IsTrue((0.0 <= P) && (P <= 1.0));
Assert.IsTrue((0.0 <= Q) && (Q <= 1.0));
Assert.IsTrue(TestUtilities.IsNearlyEqual(P + Q, 1.0));
double p = distribution.ProbabilityDensity(x);
Assert.IsTrue(p >= 0.0);
}
private void DistributionProbabilityTestHelper(ContinuousDistribution distribution, double x)
{
double P = distribution.LeftProbability(x);
double Q = distribution.RightProbability(x);
Assert.IsTrue((0.0 <= P) && (P <= 1.0));
Assert.IsTrue((0.0 <= Q) && (Q <= 1.0));
Assert.IsTrue(TestUtilities.IsNearlyEqual(P + Q, 1.0));
double p = distribution.ProbabilityDensity(x);
Assert.IsTrue(p >= 0.0);
double h = distribution.Hazard(x);
if (p > 0.0 && Q > 0.0)
{
Assert.IsTrue(TestUtilities.IsNearlyEqual(h, p / Q));
}
}
private static void ComputeDStatistics(IReadOnlyList <double> sample, ContinuousDistribution distribution, out double D1, out double D2)
{
List <int> order = GetSortOrder(sample);
D1 = 0.0;
D2 = 0.0;
double P1 = 0.0;
for (int i = 0; i < sample.Count; i++)
{
// Compute the theoretical CDF value at the data-point.
double x = sample[order[i]];
double P = distribution.LeftProbability(x);
// compute the experimental CDF value at x+dx
double P2 = (i + 1.0) / sample.Count;
// Compare the the theoretical and experimental CDF values at x-dx and x+dx;
// Update D if it is the largest seperation yet observed.
double DU = P2 - P;
if (DU > D1)
{
D1 = DU;
}
double DL = P - P1;
if (DL > D2)
{
D2 = DL;
}
// Remember the experimental CDF value at x+dx;
// it will be the experimental CDF value at x-dx for the next step
P1 = P2;
}
}