# kriging.py¶

Surrogate model based on Kriging.

class openmdao.surrogate_models.kriging.KrigingSurrogate(**kwargs)[source]

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: scikit-learn).

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.
__init__(**kwargs)[source]

Initialize all attributes.

Parameters
**kwargsdict

options dictionary.

linearize(x)[source]

Calculate the jacobian of the Kriging surface at the requested point.

Parameters
xarray-like

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
xarray-like

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
xarray-like

Training input locations

yarray-like

Model responses at given inputs.

vectorized_predict(x)

Calculate predicted values of the response based on the current trained model.

Parameters
xarray-like

Vectorized point(s) at which the surrogate is evaluated.