Hi, I’ve a multi dimensional dataset (3 features and 1 label) and want to interpolate using cubic splines. I have found several documentations on interpolation in 2D but can’t find anything that’ll help with this. Looking forward to any suggestions.
Is the data on a cartesian grid? If so, it should be straightforward to recursively define a 1d spline of 1d splines of 1d splines (by making splines of the coefficients)…
Can you mention any resources that’ll help me implement this effectively. My data is structured such that
M*g is the label. I’m showing a few points but I’ve around 18k of them.
# a/c a/t phi M*g
0 0.2 0.2 0.162 1.24187
1 0.2 0.2 0.261 1.20996
2 0.2 0.2 0.423 1.18170
3 0.2 0.2 0.581 1.15911
4 0.2 0.2 0.739 1.16206
Radial Basis Functions are a common way to do multidimensional interpolation or more generally function approximation.
They are also relatively easy to understand usually infinitely smooth, and have nice rapid convergence properties for many cases.