def cmpt_Y():
Y = np.zeros((Ma, Mb, Mc, N))
for i in range(Ma):
for k in range (Mb):
for m in range(Mc):
Y[i, k, m, :] = evaluate(param_values, delta, np.array([alpha[0, i], alpha[1, k], alpha[2, m]]), np.array([a[0, i], a[1, k], a[2, m]]))
return Y
def cmpt_Y_C(N):
print('Computing system output...')
# Sampling from the stratum using LHS251
problem = {
'nvars': 3,
'names': ['x1', 'y1', 'z1'],
'bounds': [[0, 1], [0, 1], [0, 1]],
'dists': ['UNIFORM', 'UNIFORM', 'UNIFORM']
}
param_values = lhs(problem, N, seed=933090934)
Y = np.zeros((Ma, Mb, N, Mc, N), dtype=np.float32)
for i in range(Ma):
for k in range(Mb):
if (i == 0 and k == 0):
theta_non_K_set = param_values[:, [0, 1]]
for j in range(N):
for m in range(Mc):
if m == 0:
theta_K_set = param_values[:, 0]
else:
theta_K_set = param_values[:, 0]
values = np.hstack(
(np.repeat(theta_non_K_set[j, :].reshape(1, -1),
N,
axis=0), theta_K_set.reshape(-1, 1)))
Y[i, k, j, m, :] = evaluate(
values, delta,
np.array([alpha[0, i], alpha[1, k], alpha[2, m]]),
np.array([a[0, i], a[1, k], a[2, m]]))
if (i == 0 and k == 1):
theta_non_K_set = param_values[:, [0, 1]]
for j in range(N):
for m in range(Mc):
if m == 0:
theta_K_set = param_values[:, 0]
else:
theta_K_set = param_values[:, 0]
values = np.hstack(
(np.repeat(theta_non_K_set[j, :].reshape(1, -1),
N,
axis=0), theta_K_set.reshape(-1, 1)))
Y[i, k, j, m, :] = evaluate(
values, delta,
np.array([alpha[0, i], alpha[1, k], alpha[2, m]]),
np.array([a[0, i], a[1, k], a[2, m]]))
if (i == 1 and k == 0):
theta_non_K_set = param_values[:, [0, 1]]
for j in range(N):
for m in range(Mc):
if m == 0:
theta_K_set = param_values[:, 0]
else:
theta_K_set = param_values[:, 0]
values = np.hstack(
(np.repeat(theta_non_K_set[j, :].reshape(1, -1),
N,
axis=0), theta_K_set.reshape(-1, 1)))
Y[i, k, j, m, :] = evaluate(
values, delta,
np.array([alpha[0, i], alpha[1, k], alpha[2, m]]),
np.array([a[0, i], a[1, k], a[2, m]]))
if (i == 1 and k == 1):
theta_non_K_set = param_values[:, [0, 1]]
for j in range(N):
for m in range(Mc):
if m == 0:
theta_K_set = param_values[:, 0]
else:
theta_K_set = param_values[:, 0]
values = np.hstack(
(np.repeat(theta_non_K_set[j, :].reshape(1, -1),
N,
axis=0), theta_K_set.reshape(-1, 1)))
Y[i, k, j, m, :] = evaluate(
values, delta,
np.array([alpha[0, i], alpha[1, k], alpha[2, m]]),
np.array([a[0, i], a[1, k], a[2, m]]))
print('Numerical mean Y =', np.mean(Y))
return Y
for imb in range(Mb):
for imc in range(Mc):
print('Current system model: ima=%d, imb=%d, imc=%d' %
(ima, imb, imc))
Y1 = np.zeros((N, N, N))
for i in range(N):
for j in range(N):
for k in range(N):
values = np.array([
param_values[i][0], param_values[j][1],
param_values[k][2]
]).reshape(1, 3)
Y1[i, j, k] = evaluate(
values, delta,
np.array(
[alpha[0, ima], alpha[1, imb], alpha[2, imc]]),
np.array([a[0, ima], a[1, imb], a[2, imc]]))
# Save the model outputs
# np.save('D:\Y1_500.npy', Y1)
# Calculate the mean and variance of the differences for g1*
diffA = np.zeros(N * N * N * N)
it = 0
for j in range(N):
for k in range(N):
for i1 in range(N):
for i2 in range(N):
diffA[it] = Y1[i1, j, k] - Y1[i2, j, k]
it = it + 1