kriging.py

Surrogate model based on Kriging.

class openmdao.surrogate_models.kriging.FloatKrigingSurrogate(nugget=2.2204460492503131e-15)

Bases: openmdao.surrogate_models.kriging.KrigingSurrogate

Surrogate model based on the simple Kriging interpolation. Predictions are returned as floats, which are the mean of the model’s prediction.

predict(x)

class openmdao.surrogate_models.kriging.KrigingSurrogate(nugget=2.2204460492503131e-15)

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: scikit-learn).

Args:

nugget : double or ndarray, optional

Nugget smoothing parameter for smoothing noisy data. Represents the variance of the input values. If nugget is an ndarray, it must be of the same length as the number of training points. Default: 10. * Machine Epsilon

linearize(x)

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

Args:

x : array-like

Point at which the surrogate Jacobian is evaluated.

predict(x, eval_rmse=True)

Calculates a predicted value of the response based on the current trained model for the supplied list of inputs.

Args:

x : array-like

Point at which the surrogate is evaluated.

eval_rmse : bool

Flag indicating whether the Root Mean Squared Error (RMSE) should be computed.

train(x, y)

Train the surrogate model with the given set of inputs and outputs.

Args:

x : array-like

Training input locations

y : array-like

Model responses at given inputs.