def test_dists_to_ot():
dists = dists_to_ot(['Uniform(12, 15)', 'Normal(400, 10)'])
out = [ot.Uniform(12, 15), ot.Normal(400, 10)]
assert dists == out
with pytest.raises(AttributeError):
dists_to_ot(['Uniorm(12, 15)'])
print("Samling error (y_train1) Study#" + str(i))
x_c, y, q = fl(x_train1[i])
y_train1.append(y)
for i in range(len(x_trainr)):
print("Reference case for LC metrics (y_trainr) Study#" + str(i))
x_c, y, q = fl(x_trainr[i])
y_trainr.append(y)
# Build the test sample
test_size = 1000 # test size
dists = [
'Uniform(20., 40.)', 'BetaMuSigma(2000, 500, 1000, 3000).getDistribution()'
]
dists_ot = dists_to_ot(dists)
x_test = ot.LHSExperiment(ot.ComposedDistribution(dists_ot), test_size, True,
True).generate()
x_test = np.array(x_test)
#Buil the ouput test data
for i in range(len(x_test)):
print("Test#" + str(i))
x_c, y, q = fl(x_test[i])
y_test.append(y)
# Surrogate
## Polynomial Chaos
### Quad
59801.53846153847, 59908.461538461546, 60015.384615384624,
60122.3076923077, 60229.23076923078, 60336.15384615386, 60443.07692307694,
60550.0, 60654.545454545456, 60759.09090909091, 60863.63636363637,
60968.18181818182, 61072.72727272728, 61177.272727272735,
61281.81818181819, 61386.36363636365, 61490.9090909091, 61595.45454545456,
61700.0, 61818.75, 61937.5, 62056.25, 62175.0
]
in_dim = len(corners) # input dim
Nl = 1000 # learning sample size
Nt = 1000 # test sample size
plabels = ['Ks_{min1}', 'Ks_{min2}', 'Ks_{min3}', 'Q']
dists = [
'BetaMuSigma(4031, 400, 1000, 6000).getDistribution()',
'Uniform(15., 60.)', 'Uniform(15., 60.)', 'Uniform(15., 60.)'
]
distsOT = dists_to_ot(dists)
space = Space(corners)
#Get the Data Base for UQ
Case = Mascaret_new()
X = Case.data_input
x_l = X[0:799, :]
x_t = X[800:999, :]
# Build the learning sample
#x_l = ot.LHSExperiment(ot.ComposedDistribution(distsOT), Nl, True, True).generate() #LHS distribution
#x_l = [list(x_l[i]) for i in range(Nl)]
#x_l = np.array(x_l)
doe_l = doe(x_l)
doe_t = doe(x_t)