private static PositiveDefiniteMatrix GetInverse(PositiveDefiniteMatrix A)
{
PositiveDefiniteMatrix result = new PositiveDefiniteMatrix(A.Rows, A.Cols);
LowerTriangularMatrix L = new LowerTriangularMatrix(A.Rows, A.Cols);
L.SetToCholesky(A);
bool[] isZero = new bool[L.Rows];
for (int i = 0; i < L.Rows; i++)
{
if (L[i, i] == 0)
{
isZero[i] = true;
L[i, i] = 1;
}
}
L.SetToInverse(L);
result.SetToOuterTranspose(L);
for (int i = 0; i < isZero.Length; i++)
{
if (isZero[i])
{
result[i, i] = double.PositiveInfinity;
}
}
return(result);
}
public static double[] Logh([SkipIfUniform] Gamma exp, [Proper] Gaussian d, double[] result)
{
if (dd == null) dd = Deriv2Matrix(n, 1/(inc*inc));
double b = exp.Rate;
double dPrec = d.Precision;
DenseVector aux = DenseVector.Zero(n);
PositiveDefiniteMatrix denom = new PositiveDefiniteMatrix(n, n);
denom.SetToOuterTranspose(dd);
for (int i = 0; i < n; i++) {
denom[i, i] += 1;
}
DenseVector logh = DenseVector.Zero(n);
for (int i = 0; i < n; i++) {
double x = xmin + i*inc;
logh[i] = Math.Log(dPrec + b*Math.Exp(x));
}
for (int iter = 0; iter < 100; iter++) {
if (iter == 0) aux.SetToProduct(dd, logh);
aux = Aux(exp, d, logh, aux);
logh.SetToProduct(aux, dd);
for (int i = 0; i < n; i++) {
double x = xmin + i*inc;
double target = dPrec + b*Math.Exp(x) + 0.5*aux[i];
if (target < 0) throw new Exception();
logh[i] += Math.Log(target);
}
logh.PredivideBy(denom);
}
for (int i = 0; i < n; i++) {
result[i] = logh[i];
}
return result;
}
