autoemulate.emulators.support_vector_machines

autoemulate.emulators.support_vector_machines#

class SupportVectorMachines(kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1, normalise_y=True)[source]#

Bases: BaseEstimator, RegressorMixin

Support Vector Machines Emulator.

Wraps Support Vector Regressor from scikit-learn.

fit(X, y)[source]#

Fits the emulator to the data.

Parameters:

  • X ({array-like, sparse matrix}, shape (n_samples, n_features)) – The training input samples.

  • y (array-like, shape (n_samples,) or (n_samples, n_outputs)) – The target values (real numbers).

Returns:

self – Returns self.

Return type:

object

predict(X)[source]#

Predicts the output for a given input.

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features)) – The training input samples.

Returns:

y – The predicted output values.

Return type:

array-like, shape (n_samples,) or (n_samples, n_outputs)

get_grid_params(search_type='random')[source]#

Returns the grid paramaters for the emulator.