CODEWITHSUNDEEP
pythonrjuliaspline
Adam Ryczkowski
Adam Ryczkowski

Reputation: 8074

Spline approximation of a function with adaptive knot selection

I have a problem: I have an expensive to compute 1D function (float->float). I want to approximate it with splines for efficiency.

I know I can define a set of knot points with a uniform grid on the function's domain, evaluate the function on that grid and calculate a spline over that set. But the functions are special - they have huge dull regions and some special places with complex behavior. I want an algorithm that adaptively finds some optimal set of the knot points, sapling them more densely where the function has a difficult shape, and less so when the spline makes a good job in approximating it.

How can I find a library (preferably Python, but at this point I will take anything open source) that does it with an automatic knot selection? I tried many Google searches and still found nothing

Upvotes: 2

Views: 879

Answers (1)

Adam Ryczkowski
Adam Ryczkowski

Reputation: 8074

I have finally found one, that seems to be working. It's splipy. It's method fit produces a B-Spline interpolation of the function object, with a relatively precise way to control the accuracy of the interpolation.

The source is on GitHub.

Upvotes: 0

Related Questions