def cov_bonilla(X0, Y, theta):
#Kx = mk52(np.array(X0)[:, 1:], [theta.l1, theta.l2], theta.sig_m)
Kx = squared(np.array(X0)[:, 1:], [theta.l1, theta.l2], theta.sig_m)
Kx = Kx + 1E-6 * np.eye(Kx.shape[0])
if unitary is not None:
Ks = np.array([[1.0, float(unitary)], [float(unitary), 1.0]])
else:
L = np.array([
np.array([theta.rho[str(sorted([i, j]))] if i >= j else 0.0 for j in range(theta.n_IS)]) # Lower triangulary
for i in range(theta.n_IS)
])
Ks = L.dot(L.T)
e = np.diag(np.array([theta.e1, theta.e2]))
Ks = np.matmul(e, np.matmul(Ks, e))
K = np.kron(Ks, Kx)
return np.kron(Ks, Kx)
def cov_bonilla(X0, Y, theta):
Kx = squared(np.array(X0)[:, 1:], [theta.l1, theta.l2], theta.sig_1)
Kx = Kx + 1E-6 * np.eye(Kx.shape[0])
if use_J:
Ks = np.ones((theta.n_IS, theta.n_IS)) * (1.0 - 1E-6) + np.eye(
theta.n_IS) * 1E-6
elif use_I:
Ks = np.eye(theta.n_IS)
elif dpc and invert_dpc:
Ks = np.array([
np.array([
1.0 if i != j else theta.rho["[0, %d]" % i]**(-2.0)
for j in range(theta.n_IS)
]) for i in range(theta.n_IS)
])
elif dpc:
Ks = np.array([
np.array([
theta.rho[str(sorted([i, j]))] for j in range(theta.n_IS)
]) for i in range(theta.n_IS)
])
else:
L = np.array([
np.array([
theta.rho[str(sorted([i, j]))] if i >= j else 0.0
for j in range(theta.n_IS)
]) for i in range(theta.n_IS)
])
# Force it to be positive semi-definite
Ks = L.dot(L.T)
if theta.n_IS == 2:
e = np.diag(np.array([theta.e1, theta.e2]))
elif theta.n_IS == 3:
e = np.diag(np.array([theta.e1, theta.e2, theta.e3]))
else:
raise Exception("HOW?")
Ks = e.dot(Ks.dot(e))
return np.kron(Ks, Kx)
def cov_pricm(X0, Y, theta, split=False):
Kx = squared(np.array(X0), [theta.l1], theta.sig_1)
Kx = Kx + 1E-6 * np.eye(Kx.shape[0])
if model.lower() == "pricm":
Ks = np.array([
np.array([
theta.rho[str(sorted([i, j]))] for j in range(theta.n_IS)
]) for i in range(theta.n_IS)
])
elif model.lower() == "icm":
L = np.array([
np.array([
theta.rho[str(sorted([i, j]))] if i >= j else 0.0
for j in range(theta.n_IS)
]) for i in range(theta.n_IS)
])
# Force it to be positive semi-definite
Ks = L.dot(L.T)
if split:
return Ks, Kx
else:
return np.kron(Ks, Kx)
def cov_miso(X0, Y, theta, split=False):
Kx = squared(np.array(X0), [theta.l1], theta.sig_1)
Kx_l = squared(np.array(X0), [theta.l2], theta.sig_2)
return np.block([[Kx, Kx], [Kx, Kx + Kx_l]])
def cov(X0, Y, theta):
return squared(np.array(X0), [theta.l1], theta.sig_1)
def cov(X0, Y, theta):
return squared(np.array(X0)[:, 1:], [theta.l1, theta.l2], theta.sig_1)
def cov_miso(X0, Y, theta):
Kx = squared(np.array(X0)[:, 1:], [theta.l1, theta.l2], theta.sig_1)
Kx_l = squared(np.array(X0)[:, 1:], [theta.l3, theta.l4], theta.sig_2)
return np.block([[Kx, Kx], [Kx, Kx + Kx_l]])