aDesign.getResult().getDesignOfExperiment().getOutputSample(), 1e-16)
# Taylor Expansions ##
taylorExpansionsMoments = persalys.TaylorExpansionMomentsAnalysis(
'myTaylorExpansionMoments', model)
myStudy.add(taylorExpansionsMoments)
taylorExpansionsMoments.run()
taylorExpansionsMomentsResult = taylorExpansionsMoments.getResult()
# Comparaison
openturns.testing.assert_almost_equal(
0.059730458221,
taylorExpansionsMomentsResult.getMeanFirstOrder()[0], 1e-13)
# Monte Carlo ##
montecarlo = persalys.MonteCarloAnalysis('myMonteCarlo', model)
montecarlo.setMaximumCalls(1000)
montecarlo.setMaximumCoefficientOfVariation(-1)
myStudy.add(montecarlo)
montecarlo.run()
montecarloResult = montecarlo.getResult()
# Comparaison
openturns.testing.assert_almost_equal(0.0597109963361,
montecarloResult.getMean()[3][0], 1e-13)
openturns.testing.assert_almost_equal(
0.0114128746587,
montecarloResult.getStandardDeviation()[3][0], 1e-13)
meanCI = montecarloResult.getMeanConfidenceInterval()
openturns.testing.assert_almost_equal(0.0590036320343,
# Model
X0 = persalys.Input('X0', ot.Normal(1, 1))
X1 = persalys.Input('X1', ot.Normal(1, 1))
Y00 = persalys.Output('fake_Y0')
Y00.setIsSelected(False)
Y0 = persalys.Output('Y0')
formula_Y00 = 'X0'
formula_Y0 = 'sin(X0) + 8*X1'
model = persalys.SymbolicPhysicalModel('aModelPhys', [X0, X1], [Y00, Y0],
[formula_Y00, formula_Y0])
myStudy.add(model)
# Monte Carlo ##
analysis = persalys.MonteCarloAnalysis('myMonteCarlo', model)
analysis.setLevelConfidenceInterval(0.93)
analysis.setMaximumCalls(1000)
analysis.setMaximumCoefficientOfVariation(-1)
analysis.setMaximumElapsedTime(30)
analysis.setSeed(2)
myStudy.add(analysis)
print(analysis)
analysis.run()
result = analysis.getResult()
print("result=", result)
print("PDF=", result.getPDF())
print("CDF=", result.getCDF())
print("outliers=", result.getOutliers())
kriging.setInterestVariables(['y0', 'y1'])
myStudy.add(kriging)
# 1-b Chaos ##
chaos1 = persalys.FunctionalChaosAnalysis('chaos_1', probaDesign)
chaos1.setChaosDegree(7)
chaos1.setSparseChaos(True)
chaos1.setTestSampleValidation(True)
chaos1.setKFoldValidation(True)
chaos1.setInterestVariables(['y1'])
myStudy.add(chaos1)
# 2- central tendancy ##
# 2-a Monte Carlo ##
monteCarlo = persalys.MonteCarloAnalysis('MonteCarlo', model1)
monteCarlo.setIsConfidenceIntervalRequired(False)
monteCarlo.setMaximumCoefficientOfVariation(-1.)
monteCarlo.setMaximumElapsedTime(1000)
monteCarlo.setMaximumCalls(20)
monteCarlo.setSeed(2)
monteCarlo.setInterestVariables(['y0', 'y1'])
myStudy.add(monteCarlo)
# 2-b Taylor Expansion ##
taylor = persalys.TaylorExpansionMomentsAnalysis('Taylor', model1)
taylor.setInterestVariables(['y0', 'y1'])
myStudy.add(taylor)
# 2-c Taylor Expansion which generate an error
taylor2 = persalys.TaylorExpansionMomentsAnalysis('Taylor2', model1)
