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,
