def calculate_variance(X, Xs):
kernel = RBF()
kernel.__init__()
sigma = 0.01
ks = kernel.diag(Xs)
Kt = kernel.__call__(X, X) + sigma**2
if is_invertible(Kt):
Kt = inv(Kt)
else:
Kt = pinv(Kt)
K = kernel.__call__(Xs, X)
Ktt = np.matmul(K, Kt)
Ktt = np.matmul(Ktt, K.transpose())
Ktt = Ktt.diagonal()
var = ks - Ktt.transpose()
return var
def __init__(self, length_scale=1.0, length_scale_bounds=(1e-5, 1e5), nu=1.5, int_dim=None):
RBF.__init__(self, length_scale, length_scale_bounds)
self.nu = nu
self.int_dim = int_dim
