public virtual Matrix integratedCovariance(double t, Vector x)
{
// this implementation is not intended for production.
// because it is too slow and too inefficient.
// This method is useful for testing and R&D.
// Please overload the method within derived classes.
//QL_REQUIRE(x.empty(), "can not handle given x here");
try {
if (!(x.empty()))
{
throw new ApplicationException("can not handle given x here");
}
}
catch { } //x is empty or null
Matrix tmp = new Matrix(size_, size_, 0.0);
for (int i = 0; i < size_; ++i)
{
for (int j = 0; j <= i; ++j)
{
Var_Helper helper = new Var_Helper(this, i, j);
GaussKronrodAdaptive integrator = new GaussKronrodAdaptive(1e-10, 10000);
for (int k = 0; k < 64; ++k)
{
tmp[i, j] += integrator.value(helper.value, k * t / 64.0, (k + 1) * t / 64.0);
}
tmp[j, i] = tmp[i, j];
}
}
return(tmp);
}
public virtual Matrix integratedCovariance(double t, Vector x = null)
{
// this implementation is not intended for production.
// because it is too slow and too inefficient.
// This method is useful for testing and R&D.
// Please overload the method within derived classes.
Utils.QL_REQUIRE(x == null ,()=> "can not handle given x here");
Matrix tmp= new Matrix(size_, size_,0.0);
for (int i=0; i<size_; ++i) {
for (int j=0; j<=i;++j) {
Var_Helper helper = new Var_Helper(this, i, j);
GaussKronrodAdaptive integrator=new GaussKronrodAdaptive(1e-10, 10000);
for (int k=0; k < 64; ++k) {
tmp[i,j] +=integrator.value(helper.value, k * t / 64.0, (k + 1) * t / 64.0);
}
tmp[j,i]=tmp[i,j];
}
}
return tmp;
}
