kriging.py¶
Surrogate model based on Kriging.

class
openmdao.surrogate_models.kriging.
KrigingSurrogate
(**kwargs)[source]¶ Bases:
openmdao.surrogate_models.surrogate_model.SurrogateModel
Surrogate Modeling method based on the simple Kriging interpolation.
Predictions are returned as a tuple of mean and RMSE. Based on Gaussian Processes for Machine Learning (GPML) by Rasmussen and Williams. (see also: scikitlearn).
Attributes
alpha
(ndarray) Reduced likelihood parameter: alpha
L
(ndarray) Reduced likelihood parameter: L
n_dims
(int) Number of independents in the surrogate
n_samples
(int) Number of training points.
sigma2
(ndarray) Reduced likelihood parameter: sigma squared
thetas
(ndarray) Kriging hyperparameters.
X
(ndarray) Training input values, normalized.
X_mean
(ndarray) Mean of training input values, normalized.
X_std
(ndarray) Standard deviation of training input values, normalized.
Y
(ndarray) Training model response values, normalized.
Y_mean
(ndarray) Mean of training model response values, normalized.
Y_std
(ndarray) Standard deviation of training model response values, normalized.

linearize
(x)[source]¶ Calculate the jacobian of the Kriging surface at the requested point.
 Parameters
 xarraylike
Point at which the surrogate Jacobian is evaluated.
 Returns
 ndarray
Jacobian of surrogate output wrt inputs.

predict
(x)[source]¶ Calculate predicted value of the response based on the current trained model.
 Parameters
 xarraylike
Point at which the surrogate is evaluated.
 Returns
 ndarray
Kriging prediction.
 ndarray, optional (if eval_rmse is True)
Root mean square of the prediction error.

train
(x, y)[source]¶ Train the surrogate model with the given set of inputs and outputs.
 Parameters
 xarraylike
Training input locations
 yarraylike
Model responses at given inputs.

vectorized_predict
(x)¶ Calculate predicted values of the response based on the current trained model.
 Parameters
 xarraylike
Vectorized point(s) at which the surrogate is evaluated.
