sobol.setReplicationSize(200)
sobol.setMaximumCalls(1000)
myStudy.add(sobol)
sobol.run()
sobolResult = sobol.getResult()
# Comparaison
firstOrderIndicesValues = [[0.643987, 0.0183602, 0.255834]]
totalIndicesValues = [[0.610267, 0.0494237, 0.280706]]
openturns.testing.assert_almost_equal(firstOrderIndicesValues,
sobolResult.getFirstOrderIndices(), 1e-6)
openturns.testing.assert_almost_equal(totalIndicesValues,
sobolResult.getTotalIndices(), 1e-6)
# SRC ##
src = persalys.SRCAnalysis('mySRC', model)
myStudy.add(src)
src.run()
srcResult = src.getResult()
# Comparaison
openturns.testing.assert_almost_equal(0.628946,
srcResult.getIndices()[0][0], 1e-5)
openturns.testing.assert_almost_equal(0.0476118,
srcResult.getIndices()[0][1], 1e-5)
openturns.testing.assert_almost_equal(0.318226,
srcResult.getIndices()[0][2], 1e-5)
# Chaos ##
values = [[
10035.5, 10072.1, 10108.6, 10145.2, 10181.7, 10218.3, 10254.8, 10291.4,
# Model
x0 = persalys.Input('x0', ot.Normal())
x1 = persalys.Input('x1', ot.Normal())
y00 = persalys.Output('fake_y0')
y00.setIsSelected(False)
y0 = persalys.Output('y0')
formula_y00 = 'x0'
formula_y0 = '10+3*x0+x1'
model = persalys.SymbolicPhysicalModel('aModel', [x0, x1], [y00, y0],
[formula_y00, formula_y0])
myStudy.add(model)
# SRC ##
analysis = persalys.SRCAnalysis('aSRC', model)
analysis.setSimulationsNumber(1000)
analysis.setSeed(2)
myStudy.add(analysis)
print(analysis)
analysis.run()
print("result=", analysis.getResult())
# SRC ##
X2 = persalys.Input('x2', 10)
model.addInput(X2)
model.addOutput(persalys.Output('y1'))
model.setFormula('y1', '3*x0 + x1 + x2')
# 4- sensitivity ##
# 4-a Sobol ##
sobol = persalys.SobolAnalysis('Sobol', model1)
sobol.setMaximumConfidenceIntervalLength(-1)
sobol.setMaximumElapsedTime(1000)
sobol.setMaximumCalls(20)
sobol.setReplicationSize(3)
sobol.setBlockSize(1)
sobol.setSeed(2)
sobol.setInterestVariables(['y0', 'y1'])
myStudy.add(sobol)
# 4-b SRC ##
src = persalys.SRCAnalysis('SRC', model1)
src.setSimulationsNumber(20)
src.setSeed(2)
src.setInterestVariables(['y0', 'y1'])
myStudy.add(src)
# 7- data analysis ##
dataAnalysis = persalys.DataAnalysis('DataAnalysis', model3)
myStudy.add(dataAnalysis)
# 8- Marginals inference ##
inference = persalys.InferenceAnalysis('inference', model3)
inference.setInterestVariables(['x_0', 'x_3'])
factories = [ot.NormalFactory(), ot.GumbelFactory()]
inference.setDistributionsFactories('x_3', factories)
inference.setLevel(0.1)