list_sampleSize = np.logspace(1, 5,
n_loops) # n_loops valeurs de 1e1 à 1e5
ref_sampleSize = 4e5
ref_nrepetitions = 1000
################################################################################
################# GSOBOL #####################
################################################################################
a = array([0, 9, 99])
# Distribution uniforme associée au cas-test GSobol
distribution_gsobol = gsobolDistribution(len(a))
model_gsobol = ot.PythonFunction(len(a),
1,
func_sample=lambda X: gsobol(X, a))
model_gsobol.setName("G-Sobol")
# Saltelli
runConvergence(model_gsobol, distribution_gsobol,
SaltelliSensitivityAlgorithm, list_sampleSize,
ref_sampleSize, ref_nrepetitions)
# Jansen
runConvergence(model_gsobol, distribution_gsobol,
JansenSensitivityAlgorithm, list_sampleSize, ref_sampleSize,
ref_nrepetitions)
# Mauntz
runConvergence(model_gsobol, distribution_gsobol,
MauntzKucherenkoSensitivityAlgorithm, list_sampleSize,
# Taille du plan d'expérience de base pour estimer S et ST
sampleSize = 1000
# Nombre de répétition de l'expérience
nrepetitions = 500
# Estimations des indices du premier ordre
sampleFirstMartinez = ot.Sample(nrepetitions, 3)
# Estimations des indices totaux
sampleTotalMartinez = ot.Sample(nrepetitions, 3)
for i in range(nrepetitions):
inputDesign = ot.SobolIndicesExperiment(distribution,
sampleSize).generate()
outputDesign = gsobol(inputDesign, a)
sensitivity_algorithm = ot.SaltelliSensitivityAlgorithm(
inputDesign, outputDesign, sampleSize)
fo = sensitivity_algorithm.getFirstOrderIndices()
to = sensitivity_algorithm.getTotalOrderIndices()
for j in range(d):
sampleFirstMartinez[i, j] = fo[j]
for j in range(d):
sampleTotalMartinez[i, j] = to[j]
fig = pl.figure(figsize=(12, 8))
for j in range(d):
ax = fig.add_subplot(2, 3, 1 + j)
graph = ot.HistogramFactory().build(sampleFirstMartinez[:, j]).drawPDF()
graph.setXTitle("S%d" % (d))
graph.setLegends([""])
if __name__ == "__main__":
# Sizes of simulation
n_loops = 10
list_sampleSize_float = np.logspace(1, 5, n_loops) # n_loops valeurs de 1e1 à 1e5
list_sampleSize = [int(v) for v in list_sampleSize_float]
ref_sampleSize = 4e5
ref_nrepetitions = 1000
# GSOBOL
a = array([0, 9, 99])
# Distribution uniforme associée au cas-test GSobol
distribution_gsobol = gsobolDistribution(len(a))
model_gsobol = ot.PythonFunction(len(a), 1, func_sample=lambda X: gsobol(X, a))
model_gsobol.setName("G-Sobol")
# Saltelli
runConvergence(
model_gsobol,
distribution_gsobol,
SaltelliSensitivityAlgorithm,
list_sampleSize,
)
# Jansen
runConvergence(
model_gsobol,
distribution_gsobol,
JansenSensitivityAlgorithm,