forum.alglib.net

Hi all. First off sorry for my poor english. Second... Thank you very much for this fantastic project. Alglib seems to be powerful and efficient and it must be a lot of work behind it, sure.

I'm an italian student and I'm using Alglib in order to interpolate some metereological information. I.e. I have some scattered wheater points, that could be scalar (such as temperature) or vector. These point are not gridded and the distance between them is random. I'd like to obtain a prediction of these information on arbitrary points in the 3d space.

I was looking for something interesting and I've tried to use alglib in order to solve this problem, but:

- my idea was to use an inverse distance weighted interpolation, but i see that is considered deprecated and replaced by RBF algorithms.

- I've tried to use RBF algorithms, but I can't obtain the expected result. I've tried QNM and Multilayer algorithms and I was stuck in the examples. I don't realize why, for example, when i try to use the example of multilayer, when I try to evaluate the function in points placed so far from the scattered-input-data-points (i.e. 100,100) the prediction on these points is so far from scattered values. Evaluating the function in 100,100, for example, I have a result of -9.60. But the samples are defined by

real_2d_array xy0 = "[[-2,0,1],[-1,0,0],[0,0,1],[+1,0,-1],[+2,0,1]]";

is it possible that prediction could be so far? This result is independent from the number of iterations that i use.

Is it a normal behaviour? Have I to use some different algorithm in order to solve my problem? Splines? Or deprecated version of inverse weighted prediction?


Thank you very much for your patience.

S.