def meta_val(x_pred, y_real, x_exp, data, x_prob, a, setting, lower_bound,
upper_bound):
if len(a) == 0:
setting['exp_x'] = x_exp
setting['exp_y'] = data[:, 2]
setting['bounds'] = [(lower_bound, upper_bound),
(lower_bound, upper_bound),
(lower_bound, upper_bound)]
model = createKriging.createKriging(setting)
y_kriging, ci_kriging, var_kriging = createKriging.predictor(
x_pred, model, setting)
y_pred = y_kriging
y_Real = y_real[:, 2]
rmse, nrmse = utilPCE.validation(y_Real, y_pred)
else:
rmse = npy.zeros(len(a))
nrmse = npy.zeros(len(a))
for i in xrange(0, len(a)):
setting['exp_x'] = x_exp
setting['exp_y'] = data[i * len(x_exp):(i + 1) * len(x_exp), 2]
setting['bounds'] = [(lower_bound, upper_bound),
(lower_bound, upper_bound)]
model = createKriging.createKriging(setting)
y_kriging, ci_kriging, var_kriging = createKriging.predictor(
x_pred, model, setting)
y_pred = y_kriging
y_Real = y_real[i * len(x_pred):(i + 1) * len(x_pred), 2]
rmse[i], nrmse[i] = utilPCE.validation(y_Real, y_pred)
return rmse, nrmse
def meta_val(n_deg, x_pred, y_real, x_exp, data, x_prob, a, meta_type):
if len(a) == 0:
x_experiment = x_exp
y_experiment = data[:, 2]
PCE = calPCE.collocation(n_deg, x_prob, x_experiment, y_experiment, meta_type)
PCE_pred, y_pred = utilPCE.predictor(PCE, x_pred)
y_Real = y_real[:, 2]
rmse, nrmse = utilPCE.validation(y_Real, y_pred)
else:
rmse = npy.zeros(len(a))
nrmse = npy.zeros(len(a))
data_meta = npy.zeros((len(a) * len(x_pred), 3))
for i in xrange(0, len(a)):
x_experiment = x_exp
y_experiment = data[i * len(x_exp):(i + 1) * len(x_exp), 2]
PCE = calPCE.collocation(n_deg, x_prob, x_experiment, y_experiment, meta_type)
PCE_pred, y_pred = utilPCE.predictor(PCE, x_pred)
y_Real = y_real[i * len(x_pred):(i + 1) * len(x_pred), 2]
rmse[i], nrmse[i] = utilPCE.validation(y_Real, y_pred)
return rmse, nrmse