Fork me on GitHub

sklearn.gaussian_process.correlation_models.generalized_exponential

sklearn.gaussian_process.correlation_models.generalized_exponential(theta, d)[source]

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

                                  n
theta, d --> r(theta, d) = exp(  sum  - theta_i * |d_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).

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.

Previous