/// <summary>
/// Creates a new <see cref="GeneralContinuousDistribution"/>
/// from an existing <see cref="UnivariateContinuousDistribution">
/// continuous distribution</see>.
/// </summary>
///
/// <param name="distribution">The distribution.</param>
///
/// <returns>A <see cref="GeneralContinuousDistribution"/> representing the same
/// <paramref name="distribution"/> but whose measures and functions are computed
/// using numerical integration and differentiation.</returns>
///
public static GeneralContinuousDistribution FromDistribution(UnivariateContinuousDistribution distribution)
{
GeneralContinuousDistribution dist = new GeneralContinuousDistribution();
dist.support = distribution.Support;
dist.pdf = distribution.ProbabilityDensityFunction;
dist.cdf = distribution.DistributionFunction;
return dist;
}
private static void compare(TukeyLambdaDistribution target,
UnivariateContinuousDistribution comparison, double tol)
{
Assert.AreEqual(comparison.Mean, target.Mean);
Assert.AreEqual(comparison.Variance, target.Variance, tol);
Assert.AreEqual(comparison.Entropy, target.Entropy, 1e-4);
Assert.AreEqual(comparison.StandardDeviation, target.StandardDeviation, tol);
Assert.AreEqual(comparison.Mode, target.Mode);
Assert.AreEqual(comparison.Median, target.Median);
for (double x = -10; x < 10; x += 0.0001)
{
double actual = target.ProbabilityDensityFunction(x);
double expected = comparison.ProbabilityDensityFunction(x);
Assert.AreEqual(expected, actual, tol);
Assert.IsFalse(Double.IsNaN(actual));
}
for (double x = -10; x < 10; x += 0.0001)
{
double actual = target.DistributionFunction(x);
double expected = comparison.DistributionFunction(x);
Assert.AreEqual(expected, actual, tol);
Assert.IsFalse(Double.IsNaN(actual));
}
for (double x = -10; x < 10; x += 0.0001)
{
double actual = target.LogProbabilityDensityFunction(x);
double expected = comparison.LogProbabilityDensityFunction(x);
Assert.AreEqual(expected, actual, tol);
Assert.IsFalse(Double.IsNaN(actual));
}
}
private static void test(GeneralizedNormalDistribution target, UnivariateContinuousDistribution normal)
{
Assert.AreEqual(normal.Mean, target.Mean);
Assert.AreEqual(normal.Variance, target.Variance, 1e-10);
Assert.AreEqual(normal.Entropy, target.Entropy, 1e-10);
Assert.AreEqual(normal.StandardDeviation, target.StandardDeviation, 1e-10);
Assert.AreEqual(normal.Mode, target.Mode);
Assert.AreEqual(normal.Median, target.Median);
for (double x = -10; x < 10; x += 0.0001)
{
double actual = target.ProbabilityDensityFunction(x);
double expected = normal.ProbabilityDensityFunction(x);
Assert.AreEqual(expected, actual, 1e-15);
Assert.IsFalse(Double.IsNaN(actual));
}
for (double x = -10; x < 10; x += 0.0001)
{
double actual = target.LogProbabilityDensityFunction(x);
double expected = normal.LogProbabilityDensityFunction(x);
Assert.AreEqual(expected, actual, 1e-10);
Assert.IsFalse(Double.IsNaN(actual));
}
}
/// <summary>
/// Creates a new <see cref="GeneralContinuousDistribution"/> with the given PDF and CDF functions.
/// </summary>
///
/// <param name="distribution">A distribution whose properties will be numerically estimated.</param>
///
public GeneralContinuousDistribution(UnivariateContinuousDistribution distribution)
{
if (distribution == null)
throw new ArgumentNullException("distribution");
this.method = new InfiniteAdaptiveGaussKronrod(100);
this.pdf = distribution.ProbabilityDensityFunction;
this.cdf = distribution.DistributionFunction;
this.support = distribution.Support;
}