public void TestIntegrateDistribution(double[] x, double[] mu, double[,] sigma, double expected, double error)
{
var sampler = new MultivariateNormalSampler.Builder(mu, sigma).Build(new ReducedThreeFry4X64(1));
var actual = SamplerTester.IntegrateMultivariateCdf(sampler, x, Trials);
Assert.AreEqual(actual, expected, error);
}
public void TestConstructValid()
{
var covarianceMatrix = new[, ]
{
{ 1.0, 0.5 },
{ 0.5, 1.0 },
};
var mean = new[] { 0.0, 0.0 };
var engine = MersenneTwister32Bit19937.InitGenRand(1);
Assert.DoesNotThrow(() => _ = new MultivariateNormalSampler.Builder(mean, covarianceMatrix).Build(engine));
}
public void TestLength()
{
var mean = new[] { 0d, 0d };
var sigma = new[, ]
{
{ 1d, 0.5 },
{ 0.5, 1d },
};
var engine = MersenneTwister32Bit19937.InitGenRand(1);
var sampler = new MultivariateNormalSampler.Builder(mean, sigma).Build(engine);
Assert.AreEqual(2, sampler.Length);
}
public void TestInvalidMean()
{
var inputCovariance = new[, ]
{
{ 1.0, 0.5 },
{ 0.5, 1.0 },
};
Assert.Throws <ArgumentException>(
() => _ = new MultivariateNormalSampler.Builder(new[] { 0d }, inputCovariance));
Assert.Throws <ArgumentException>(
() => _ = new MultivariateNormalSampler.Builder(new[] { 0d, double.NaN }, inputCovariance));
Assert.Throws <ArgumentNullException>(
() => _ = new MultivariateNormalSampler.Builder(null, inputCovariance));
Assert.Throws <ArgumentNullException>(
() => _ = new MultivariateNormalSampler.Builder(new[] { 0d }, null));
}