Thanks for the great tool. Trying to find a way around "pull ins" when the regularizer has a significant low spatial frequency bias over extended areas... for example, around the top of a higly resolved gaussian peak with 'gradient'. Using a higher order regularizer doesn't help (I've implemented a 4th order gradient): that just gives a surface that's highly "puckered" around indiviual sample errors. Any ideas?
Sorry. I've never heard of a Java implementation. Not that it counts, I've written it 7 times in MATLAB, once each in Fortran and APL, those last were very old though. The difference each time was what I learned from the previous incarnation. Were someone to do a Java implementation perhaps the most important thing is to use sparse linear algebra capabilities for the solve, else it would take forever.
Just to add my $0.02 to this whole sphere discussion. By math definition of what is function it means for each x we have only one F(x). If there are more than one F(x) relation between x and F(x) is not a function.
I found that it is very straightforward to fit a nice looking surface to some 55 data-points using gridfit. I was wondering though, is there a way to get values of the surface that are not on the nodes? I wish to interpolate between my 55 samples using the surface.