sklearn.gaussian_process.correlation_models.squared_exponential

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

Squared exponential correlation model (Radial Basis Function). (Infinitely differentiable stochastic process, very smooth):

                                  n
theta, d --> r(theta, d) = exp(  sum  - theta_i * (d_i)^2 )
                                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.