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, dx --> r(theta, dx) = exp( sum - theta_i * (dx_i)^2 ) i = 1
Parameters : theta : array_like
An array with shape 1 (isotropic) or n (anisotropic) giving the autocorrelation parameter(s).
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, ) containing the values of the autocorrelation model.