sklearn.gaussian_process.correlation_models.cubic¶
- sklearn.gaussian_process.correlation_models.cubic(theta, d)[source]¶
Cubic correlation model:
theta, d --> r(theta, d) = 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).
d : 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.