GaussianProcess

class pyapprox.surrogates.GaussianProcess(kernel=None, *, alpha=1e-10, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, normalize_y=False, copy_X_train=True, random_state=None)

Bases: GaussianProcessRegressor

A Gaussian process.

Methods Summary

__call__(samples[, return_std, return_cov, ...])	A light weight wrapper of sklearn GaussianProcessRegressor.predict function.
condition_number()
fit(train_samples, train_values)	A light weight wrapper of sklearn GaussianProcessRegressor.fit function.
get_training_samples()
map_from_canonical(canonical_samples)
map_to_canonical(samples)
num_training_samples()
plot_1d(num_XX_test, bounds[, ax, ...])
predict_random_realization(samples[, ...])	Predict values of a random realization of the Gaussian process
set_variable_transformation(var_trans)

Methods Documentation