rbf.core

Radial Basis Function regression. See Radial Basis Function interpolation an regression for details.

JAX_MODE	bool(x) -> bool
Rbf(points, values[, rbf, r, p, fit])	Radial basis function network interpolation and regression.
_np_distsq(aa, bb)	(Slow) pure numpy squared distance matrix.
estimate_p(points[, method])	Estimate \(p\).
euclidean_dists(aa, bb)
jit(func, *args, **kwds)
rbf_dct	dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).
rbf_gauss(rsq, p)	Gaussian RBF \(\exp\left(-\frac{r^2}{2\,p^2}\right)\)
rbf_inv_multi(rsq, p)	Inverse Multiquadric RBF \(\frac{1}{\sqrt{r^2 + p^2}}\)
rbf_multi(rsq, p)	Multiquadric RBF \(\sqrt{r^2 + p^2}\)
squared_dists(aa, bb)