def __init__(self, X, y, convergence_rate=1.0):
self.X = X
self.y = y
self.W = None
self.K = None
self.noise_var = None
self.converge_rate_sq = np.power(convergence_rate, 2)
GaussianProcessRegressor.__init__(self, kernel=self.get_kernel(self.X))
self.fit(self.X, self.y)
self.update_W()
self.update_K(self.X)
self.update_noise_var()
def __init__(self,
kernel=None,
alpha=1e-10,
optimizer="fmin_l_bfgs_b",
n_restarts_optimizer=0,
normalize_y=False,
copy_X_train=True,
random_state=None):
_GaussianProcessRegressor.__init__(self, kernel, alpha, optimizer,
n_restarts_optimizer, normalize_y,
copy_X_train, random_state)
BaseWrapperReg.__init__(self)
def __init__(self,
kernel=None,
alpha=1e-10,
optimizer="fmin_l_bfgs_b",
n_restarts_optimizer=0,
normalize_y=False,
copy_X_train=True,
random_state=None,
prior=None):
GaussianProcessRegressor.__init__(
self,
kernel=kernel,
alpha=alpha,
optimizer="fmin_l_bfgs_b",
n_restarts_optimizer=n_restarts_optimizer,
normalize_y=False,
copy_X_train=True,
random_state=None)
self.prior = prior
