myStudy.add('formResult', formResult) # Create a SORMResult sormResult = ot.SORMResult([1.0] * 2, event, False) sormResult.setName('sormResult') sormResult.getEventProbabilityBreitung() sormResult.getEventProbabilityHohenbichler() sormResult.getEventProbabilityTvedt() sormResult.getGeneralisedReliabilityIndexBreitung() sormResult.getGeneralisedReliabilityIndexHohenbichler() sormResult.getGeneralisedReliabilityIndexTvedt() myStudy.add('sormResult', sormResult) # Create a RandomGeneratorState ot.RandomGenerator.SetSeed(0) randomGeneratorState = ot.RandomGeneratorState( ot.RandomGenerator.GetState()) myStudy.add('randomGeneratorState', randomGeneratorState) # Create a GeneralLinearModelResult generalizedLinearModelResult = ot.GeneralLinearModelResult() generalizedLinearModelResult.setName('generalizedLinearModelResult') myStudy.add('generalizedLinearModelResult', generalizedLinearModelResult) # KDTree sample = ot.Normal(3).getSample(10) kDTree = ot.KDTree(sample) myStudy.add('kDTree', kDTree) # TensorApproximationAlgorithm/Result dim = 1 model = ot.SymbolicFunction(['x'], ['x*sin(x)'])
# simulate the true physical model basis = ot.ConstantBasisFactory(4).build() covarianceModel = ot.SquaredExponential([5.03148, 13.9442, 20, 20], [15.1697]) krigingModel = ot.KrigingAlgorithm(inputSample, signals, covarianceModel, basis) ot.RandomGenerator.SetSeed(0) np.random.seed(0) krigingModel.run() physicalModel = krigingModel.getResult().getMetaModel() ####### Test on the POD models ################### # Test hitmiss without Box Cox with rf classifier np.random.seed(0) ot.RandomGenerator.SetSeed(0) ot.RandomGenerator.SetState(ot.RandomGeneratorState(ot.Indices([0] * 768), 0)) POD1 = otpod.AdaptiveHitMissPOD(inputDOE, outputDOE, physicalModel, 20, detection) POD1.run() detectionSize1 = POD1.computeDetectionSize(0.9, 0.95) def test_1_a90(): np.testing.assert_almost_equal(detectionSize1[0], 4.71811745363573, decimal=5) def test_1_a95(): np.testing.assert_almost_equal(detectionSize1[1], 5.35497504836619,