Is it possible to use the RSNNS library to perform RBF interpolation when the target output is longer than the input? about rsnns HOT 6 OPEN

Hi, I'm not sure if I understand correctly what you try to do, but it seems this is a quite standard regression problem where what you call inputs is the training set and what you call outputs is a test set. Your actual inputs in training are surf_x, surf_y, and surf_z, and fitted is the target. Then, in testing, in your "outputs" dataset, you only have surf_x, surf_y, and surf_z and want to find the "fitted" values. So what you need is something like the following to train the model:

rbf_res <- RSNNS::rbf(x = inputs[,c("surf_x","surf_y","surf_z")],
               y = inputs[,"fitted"])

Then, to predict on your new data, you need:

res = predict(rbf_res, newdata=outputs)

Any particular reason to use RBF for this? Otherwise I'd use any standard Machine Learning algorithm like a random forest or so.

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@cbergmeir

Cubic RBF interpolation has been specifically validated for my goal: https://ieeexplore.ieee.org/document/5626616

Relevant excerpt:

I'm about to leave on an extended vacation and will test this when I return, thank you!

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Sure, give it a go and let me know if it works (or not).

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Can this be used to fit a cubic or multiquadric RBF?

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It seems to have Multiquadratic, yes. Best you check in the SNNS Manual, page 175ff for the details: http://gentoo.osuosl.org/distfiles/SNNSv4.2.Manual.pdf

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There are some demos in RSNNS that should allow you to figure out how to use these parameters from SNNS in RSNNS. They are in the demo folder in the package.

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