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 $\exp (-\frac{r^2}{2p^2})$
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)