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sklearn.gaussian_process.correlation_models.cubic

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

Cubic correlation model:

theta, d --> r(theta, d) =
  n
 prod max(0, 1 - 3(theta_j*d_ij)^2 + 2(theta_j*d_ij)^3) ,  i = 1,...,m
j = 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, ) with the values of the autocorrelation model.

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