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)");
}
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");
}