nearest_neighbor.py

Surrogate model based on the N-Dimensional Interpolation library by Stephen Marone.

SMarone/NDInterp

class openmdao.surrogate_models.nearest_neighbor.NearestNeighbor(**kwargs)

Bases: SurrogateModel

Surrogate model that approximates values using a nearest neighbor approximation.

Parameters:

**kwargsdict

Options dictionary.

Attributes:

interpolantobject

Interpolator object

interpolant_init_argsdict

Input keyword arguments for the interpolator.

__init__(**kwargs)

Initialize all attributes.

Parameters:

**kwargsdict

options dictionary.

linearize(x, **kwargs)

Calculate the jacobian of the interpolant at the requested point.

Parameters:

xarray-like

Point at which the surrogate Jacobian is evaluated.

**kwargsdict

Additional keyword arguments passed to the interpolant.

Returns:

ndarray

Jacobian of surrogate output wrt inputs.

predict(x, **kwargs)

Calculate a predicted value of the response based on the current trained model.

Parameters:

xarray-like

Point(s) at which the surrogate is evaluated.

**kwargsdict

Additional keyword arguments passed to the interpolant.

Returns:

float

Predicted value.

train(x, y)

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.