Thank you very much for putting together and sharing Shape Language Modeling !
I hope you could answer one question for me. When you fit a spline into a given data (or part of it) using MLS penalty
the data is divided by X coordinate and penalty is calculated in Y coordinate.
But for many datasets, X and Y are symmetric and what I'd see as a natural penalty in these cases would be
the Minimal Least Squares of a distance from each point to the curve/spline, calculated as length of orthogonal projection of a point onto that curve. Is there a way to use slmengine to find optimal fit this way ?