sklearn.gaussian_process.correlation_models.generalized_exponential¶
- 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.):
n 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.