Developer API for surrogate models

class smt.surrogate_models.surrogate_model.SurrogateModel(**kwargs)

Base class for all surrogate models.

Examples

>>> from smt.surrogate_models import RBF
>>> sm = RBF(print_training=False)
>>> sm.options['print_prediction'] = False

Attributes:

optionsOptionsDictionary

Dictionary of options. Options values can be set on this attribute directly or they can be passed in as keyword arguments during instantiation.

supportsdict

Dictionary containing information about what this surrogate model supports.

Methods

predict_derivatives(x, kx)	Predict the dy_dx derivatives at a set of points.
predict_output_derivatives(x)	Predict the derivatives dy_dyt at a set of points.
predict_values(x)	Predict the output values at a set of points.
predict_variance_derivatives(x, kx)	Predict the derivation of the variance at a point
predict_variances(x)	Predict the variances at a set of points.
set_training_derivatives(xt, dyt_dxt, kx[, name])	Set training data (derivatives).
set_training_values(xt, yt[, name])	Set training data (values).
train()	Train the model
update_training_derivatives(dyt_dxt, kx[, name])	Update the training data (values) at the previously set input values.
update_training_values(yt[, name])	Update the training data (values) at the previously set input values.

_initialize()

Implemented by surrogate models to declare options and declare what they support (optional).

Examples

self.options.declare(‘option_name’, default_value, types=(bool, int), desc=’description’) self.supports[‘derivatives’] = True

_train() → None

Implemented by surrogate models to perform training (optional, but typically implemented).

abstract _predict_values(x: ndarray) → ndarray

Implemented by surrogate models to predict the output values.

Parameters:

xnp.ndarray[nt, nx]

Input values for the prediction points.

Returns:

ynp.ndarray[nt, ny]

Output values at the prediction points.

_predict_derivatives(x: ndarray, kx: int) → ndarray

Implemented by surrogate models to predict the dy_dx derivatives (optional).

If this method is implemented, the surrogate model should have

::

self.supports[‘derivatives’] = True

in the _initialize() implementation.

Parameters:

xnp.ndarray[nt, nx]

Input values for the prediction points.

kxint

The 0-based index of the input variable with respect to which derivatives are desired.

Returns:

dy_dxnp.ndarray[nt, ny]

Derivatives.

_predict_output_derivatives(x: ndarray) → dict

Implemented by surrogate models to predict the dy_dyt derivatives (optional).

If this method is implemented, the surrogate model should have

::

self.supports[‘output_derivatives’] = True

in the _initialize() implementation.

Parameters:

xnp.ndarray[nt, nx]

Input values for the prediction points.

Returns:

dy_dytdict of np.ndarray[nt, nt]

Dictionary of output derivatives. Key is None for derivatives wrt yt and kx for derivatives wrt dyt_dxt.

_predict_variances(x: ndarray) → ndarray

Implemented by surrogate models to predict the variances at a set of points (optional).

If this method is implemented, the surrogate model should have

::

self.supports[‘variances’] = True

in the _initialize() implementation.

Parameters:

xnp.ndarray[nt, nx]

Input values for the prediction points.

is_actingnp.ndarray[nt, nx] or np.ndarray[nt]

Matrix specifying for each design variable whether it is acting or not (for hierarchical design spaces)

Returns:

s2np.ndarray[nt, ny]

Variances.