"Linear" algorithm for griddedInterpolant
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David Epstein
am 23 Jul. 2018
Kommentiert: David Epstein
am 23 Jul. 2018
Try this
M=rand(2,3);
disp(M);
F=griddedInterpolant(M);
disp([F(1,1),F(1.5,2.5),F(2,3),F(50,19),F(-50,19)]);
What is the mysterious algorithm used by Mathworks that gives the two final answers? I need an F with predictable answers.
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Adam
am 23 Jul. 2018
What do you mean by 'predictable'? You get the same answer every time for the same inputs (i.e. obviously not creating a random input every time).
(50,19) and (-50,19) are both miles away from the input matrix though so that is a lot of extrapolation needed to get there. I don't see what is especially unpredictable about it though other than the fact that extrapolating that far from your data will always be so inaccurate as to be totally unreliable.
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Jan
am 23 Jul. 2018
Bearbeitet: Jan
am 23 Jul. 2018
Of course the output is predictable and even exactly defined as expected. If you extrapolate the values, the marginal linear segments are expanded. This is the intuitive behavior. So what exactly is the problem with griddedInterpolant? What do you expect instead?
3 Kommentare
Steven Lord
am 23 Jul. 2018
The easiest way to have the result of the extrapolation be zero is to specify that you don't want extrapolation then replace the missing values in the result of evaluating the griddedInterpolant (represented as NaN) with 0 using fillmissing or isnan.
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