This is documentation for an old release of Scikit-learn (version 0.17). Try the latest stable release (version 1.6) or development (unstable) versions.

sklearn.gaussian_process.correlation_models.absolute_exponential

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

Absolute exponential autocorrelation model. (Ornstein-Uhlenbeck stochastic process):

                                  n
theta, d --> r(theta, d) = exp(  sum  - theta_i * |d_i| )
                                i = 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, ) containing the values of the autocorrelation model.