saealib.RBFsurrogate¶

class saealib.RBFsurrogate(kernel, dim)[source]¶

Bases: Surrogate

Radial Basis Function (RBF) Interpolation surrogate model.

Supports multi-objective problems by maintaining one independent _RBFModel per objective. The number of objectives is inferred from train_y on the first call to fit (lazy initialization).

Parameters:

kernel (callable)
dim (int)

kernel¶

Kernel function (e.g. gaussian_kernel).

Type:: callable

dim¶

Dimensionality of the input data.

Type:: int

n_obj¶

Number of objectives. Set on first fit call.

Type:: int or None

Methods

`__init__`
`fit`	Fit the surrogate model.
`predict`	Predict using the surrogate model.

Method Details

RBFsurrogate.__init__(kernel, dim)[source]¶

Parameters:

kernel (callable)
dim (int)

RBFsurrogate.fit(train_x, train_y)[source]¶

Fit the surrogate model.

Parameters:

train_x (np.ndarray) – Training input data. shape: (n_samples, n_features)
train_y (np.ndarray) – Training output data. shape: (n_samples, n_obj) or (n_samples,). 1-D input is treated as single-objective: shape (n_samples, 1).

Return type:

None

RBFsurrogate.predict(test_x)[source]¶

Predict using the surrogate model.

Parameters:: test_x (np.ndarray) – Input data. shape: (n_samples, n_features) or (n_features,)
Returns:: prediction.mean shape: (n_samples, n_obj) prediction.std is None (RBF interpolation provides no uncertainty)
Return type:: SurrogatePrediction