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)'])
distribution = ot.ComposedDistribution([ot.Uniform()] * dim)
factoryCollection = [ot.FourierSeriesFactory()] * dim
functionFactory = ot.OrthogonalProductFunctionFactory(factoryCollection)
size = 10
outputSample *= scale
# translate sample
translate = [3.1]
outputSample += translate
# Finally inverse transform using an arbitrary lambda
lamb = [1.8]
boxCoxFunction = ot.InverseBoxCoxEvaluation(lamb)
# transform y using BoxCox function
outputSample = boxCoxFunction(outputSample)
# Add small noise
epsilon = ot.Normal(0, 1.0e-2).getSample(size)
outputSample += epsilon
# Now we build the factory
factory = ot.BoxCoxFactory()
# Creation of the BoxCoxTransform
result = ot.GeneralLinearModelResult()
basis = ot.LinearBasisFactory(1).build()
covarianceModel = ot.DiracCovarianceModel()
shift = [1.0e-1]
myBoxCox = factory.build(inputSample, outputSample, covarianceModel, basis,
shift, result)
print("myBoxCox (GLM) =", myBoxCox)
print("GLM result =", result)
