def fitting_gaussian_process(self, x_train, y_train):
"""
Given the training samples, fitting a gaussian process model
:param x_train: training x
:param y_train: training y
:return: the learned gaussian model
"""
kernel = C(1.0,
(1e-3, 1e3)) * Matern(length_scale=np.ones(
(feature_map().shape[0], )),
length_scale_bounds=(0.01, 10.0e20),
nu=1.5)
gpr = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=10)
gpr.fit(x_train, y_train)
gpr.optimizer = None
return gpr
