public void TestAccumulatorNumericStability()
{
/* NOTE: Statistically it is possible that this test fails even
* if everything works as expected. However, it's very unlikely to happen,
* and even more unlikely to happen in a series. */
Accumulator accumulator = new Accumulator();
NormalDistribution gaussian = new NormalDistribution();
// Test around 0, no stability issues expected
gaussian.SetDistributionParameters(0, 1);
for(int i = 0; i < 10000; i++)
{
accumulator.Add(gaussian.NextDouble());
}
Assert.That(accumulator.Mean, NumericIs.AlmostEqualTo((double) 0, 0.2), "Mean of (0,1)");
Assert.That(accumulator.Variance, NumericIs.AlmostEqualTo((double) 1, 0.5), "Variance of (0,1)");
// Test around 10^9, potential stability issues
accumulator.Clear();
gaussian.SetDistributionParameters(1e+9, 1);
for(int i = 0; i < 10000; i++)
{
accumulator.Add(gaussian.NextDouble());
}
Assert.That(accumulator.Mean, NumericIs.AlmostEqualTo(1e+9, 0.2), "Mean of (1e+9,1)");
Assert.That(accumulator.Variance, NumericIs.AlmostEqualTo((double) 1, 0.5), "Variance of (1e+9,1)");
}
/// <summary>
/// Represents the target for the adapter.
/// </summary>
//public Distributions()
//{
//}
public double NormCdf(double x)
{
double cdf;
NormalDistribution normCdf = new NormalDistribution(0, 1);
cdf = normCdf.CumulativeDistribution(x);
return cdf;
}
/// <summary>
/// Represents the target for the adapter.
/// </summary>
//public Distributions()
//{
//}
public double NormCdf(double x)
{
double cdf;
Debug.Assert(!double.IsNaN(x));
var normCdf = new NormalDistribution(0, 1);
cdf = normCdf.CumulativeDistribution(x);
return cdf;
}
/// <summary>
/// Create a gaussian noise source with normally distributed amplitudes.
/// </summary>
/// <param name="uniformWhiteRandomSource">Uniform white random source.</param>
/// <param name="mean">mu-parameter of the normal distribution</param>
/// <param name="standardDeviation">sigma-parameter of the normal distribution</param>
public WhiteGaussianNoiseSource(
RandomSource uniformWhiteRandomSource,
double mean,
double standardDeviation
)
{
NormalDistribution gaussian = new NormalDistribution(uniformWhiteRandomSource);
gaussian.SetDistributionParameters(mean, standardDeviation);
_distribution = gaussian;
}
public void TestDescriptiveStatisticsMeanVariance()
{
// Test around 10^9, potential stability issues
NormalDistribution gaussian = new NormalDistribution(1e+9, 2);
Assert.That(DescriptiveStatistics.Mean(gaussian.EnumerateDoubles(10000)), NumericIs.AlmostEqualTo(1e+9, 0.2), "Mean of (1e+9,2)");
Assert.That(DescriptiveStatistics.Variance(gaussian.EnumerateDoubles(10000)), NumericIs.AlmostEqualTo((double) 4, 0.5), "Variance of (1e+9,2)");
Assert.That(DescriptiveStatistics.StandardDeviation(gaussian.EnumerateDoubles(10000)), NumericIs.AlmostEqualTo((double) 2, 0.5), "StdDev of (1e+9,2)");
}
private double[,] InitializeDataArray(int size)
{
double[,] data = new double[size, size];
NormalDistribution normalDist = new NormalDistribution(mean, delta);
for (int i = 0; i < size; ++i) {
for (int j = 0; j < size; ++j) {
data[i, j] = normalDist.NextDouble();
}
}
return data;
}
public void AdapterConstructorTargetTest()
{
Distributions target1 = new Distributions();
Adapter target = new Adapter(target1);
Adaptee adaptee = new Adaptee();
NormalDistribution normCdf = new NormalDistribution(0, 1);
Func<double, double> expected = target1.NormCdf;
Func<double, double> actual = target.NormCdf;
Assert.AreNotEqual(expected(1), adaptee.NormCdf(1), "Should NOT be equal function behaviour.");
Assert.AreEqual(expected(1), actual(1), "Should be equal function behaviour.");
Assert.AreEqual( normCdf.CumulativeDistribution(1), actual(1), "Should be equal function behaviour.");
Func<double, double> normCdf2 = arg =>
{
arg = arg - 1;
return arg;
};
Assert.AreNotEqual(expected, normCdf2);
}
public void TestContinuousDistributions_Gaussian()
{
TestContinuousDistributionShapeMatchesCumulativeDensity(
new StandardDistribution(),
-2.0,
2.0,
10,
100000,
0.01,
"StandardDistribution()");
TestContinuousDistributionShapeMatchesCumulativeDensity(
new NormalDistribution(-5.0, 2.0),
-9.0,
-1.0,
10,
100000,
0.01,
"NormalDistribution(-5.0,2.0)");
TestContinuousDistributionShapeMatchesCumulativeDensity(
new LognormalDistribution(1.0, 0.5),
0.0,
8.0,
10,
100000,
0.01,
"LognormalDistribution(1.0,0.5)");
// Test Parameter Estimation
NormalDistribution source = new NormalDistribution(4.0, 2.0);
NormalDistribution target = new NormalDistribution();
target.EstimateDistributionParameters(source.EnumerateDoubles(1000));
Assert.That(target.Mu, NumericIs.AlmostEqualTo(4.0, 0.1), "Normal Parameter Estimation: Mu");
Assert.That(target.Sigma, NumericIs.AlmostEqualTo(2.0, 0.1), "Normal Parameter Estimation: Sigma");
}
