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
max_points_per_bin = 10 # Estimated max number of points in each bin
max_bins_total = nbins_per_dim**2 # Estimated max number of bins
bin_results = pd.DataFrame({
k: v
for k, v in zip(problem['names'], [
np.tile(np.linspace(0, 1, nbins_per_dim +
1), (problem['nvars'], 1))[i, :]
for i in range(problem['nvars'])
])
})
# Generate parameters using SALib.sample.saltelli
# param_values = saltelli.sample(problem, N, calc_second_order=False, seed=2**30)[::5, :]
# Generate parameters using LHS
param_values = lhs(problem, N, seed=933090936)
np.save('Sobol_G_param_values_binning_100.npy', param_values)
# Compute the model outputs
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
