public void CanSampleSequence()
{
var n = new InverseGaussian(1.0, 2.0);
var ied = n.Samples();
GC.KeepAlive(ied.Take(5).ToArray());
}
public void CanEstimateParameters(double mu, double lambda)
{
var original = new InverseGaussian(mu, lambda, new Numerics.Random.MersenneTwister(100));
var estimated = InverseGaussian.Estimate(original.Samples().Take(1000000));
AssertHelpers.AlmostEqualRelative(mu, estimated.Mu, 1);
AssertHelpers.AlmostEqualRelative(lambda, estimated.Lambda, 1);
}
public void CanFillSampleArray()
{
double[] samples = new double[100];
var n = new InverseGaussian(1.0, 2.0, new Numerics.Random.MersenneTwister(100));
n.Samples(samples);
Assert.IsTrue(!samples.Any(x => x == 0));
}
public void FailSampleSequenceStatic()
{
Assert.That(() => { var ied = InverseGaussian.Samples(new Numerics.Random.MersenneTwister(100), 0.0, -1.0).First(); }, Throws.ArgumentException);
}
public void FailFillingSampleArrayStatic()
{
double[] samples = new double[100];
Assert.That(() => { InverseGaussian.Samples(new Numerics.Random.MersenneTwister(100), samples, -1.0, 1.0); }, Throws.ArgumentException);
}
public void CanSampleSequenceStatic()
{
var ied = InverseGaussian.Samples(new Numerics.Random.MersenneTwister(100), 1.0, 1.0);
GC.KeepAlive(ied.Take(5).ToArray());
}
public void CanFillSampleArrayStatic()
{
double[] samples = new double[100];
InverseGaussian.Samples(new Numerics.Random.MersenneTwister(100), samples, 1.0, 1.0);
Assert.IsTrue(!samples.Any(x => x == 0));
}
