sklearn.gaussian_process.correlation_models.cubic

sklearn.gaussian_process.correlation_models.cubic(theta, d)

Cubic correlation model:

theta, dx --> r(theta, dx) =
  n
prod max(0, 1 - 3(theta_j*d_ij)^2 + 2(theta_j*d_ij)^3) ,  i = 1,...,m
j = 1

Parameters:

theta : array_like

An array with shape 1 (isotropic) or n (anisotropic) giving the autocorrelation parameter(s).

dx : array_like

An array with shape (n_eval, n_features) giving the componentwise distances between locations x and x’ at which the correlation model should be evaluated.

Returns:

r : array_like

An array with shape (n_eval, ) with the values of the autocorrelation model.