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

Generalized exponential correlation model. (Useful when one does not know the smoothness of the function to be predicted.):

theta, dx --> r(theta, dx) = exp(  sum  - theta_i * |dx_i|^p )
                                  i = 1
Parameters :

theta : array_like

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

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.