public void RandNormalP()
{
double[] m_init = { 1, 2, 3 };
Vector mt = Vector.FromArray(m_init);
double[,] v_init = { { 2, 1, 1 }, { 1, 2, 1 }, { 1, 1, 2 } };
PositiveDefiniteMatrix vt = new PositiveDefiniteMatrix(v_init);
PositiveDefiniteMatrix pt = ((PositiveDefiniteMatrix)vt.Clone()).SetToInverse(vt);
int d = mt.Count;
Vector x = Vector.Zero(d);
VectorMeanVarianceAccumulator mva = new VectorMeanVarianceAccumulator(d);
for (int i = 0; i < nsamples; i++)
{
Rand.NormalP(mt, pt, x);
mva.Add(x);
}
Vector m = mva.Mean;
PositiveDefiniteMatrix v = mva.Variance;
Console.WriteLine("");
Console.WriteLine("Multivariate NormalP");
Console.WriteLine("--------------------");
Console.WriteLine("m = {0}", m);
double dError = m.MaxDiff(mt);
if (dError > TOLERANCE)
{
Assert.True(false, String.Format("m: error = {0}", dError));
}
Console.WriteLine("v = \n{0}", v);
dError = v.MaxDiff(vt);
if (dError > TOLERANCE)
{
Assert.True(false, String.Format("v: error = {0}", dError));
}
}
