# create a function
dim = 4
function = ot.SymbolicFunction(
['E', 'F', 'L', 'I'], ['F*L^3/(3.*E*I)'])
# create a distribution
distribution = ot.Normal(
[50., 1.0, 10.0, 5.0], [1.0] * dim,
ot.IdentityMatrix(dim))
vect = ot.RandomVector(distribution)
composite = ot.CompositeRandomVector(function, vect)
event = ot.Event(composite, ot.Less(), -3.0)
# create an ADS algorithm
n = int(1e4)
algo = ot.AdaptiveDirectionalSampling(event)
algo.setMaximumOuterSampling(n)
algo.setGamma([0.6, 0.4])
algo.run()
result = algo.getResult()
print(result)
# ADS-2+
algo2 = algo
algo2.setPartialStratification(True)
algo2.run()
print('T=', algo2.getTStatistic())
result = algo2.getResult()
print(result)
# Root finding algorithm.
# %%
solver = ot.Brent()
rootStrategy = ot.MediumSafe(solver)
# %%
# Direction sampling algorithm.
# %%
samplingStrategy = ot.RandomDirection()
# %%
# Create a simulation algorithm.
# %%
algo = ot.AdaptiveDirectionalSampling(event, rootStrategy, samplingStrategy)
algo.setMaximumCoefficientOfVariation(0.1)
algo.setMaximumOuterSampling(40000)
algo.setConvergenceStrategy(ot.Full())
algo.run()
# %%
# Retrieve results.
# %%
result = algo.getResult()
probability = result.getProbabilityEstimate()
print( result )
print('Pf=', probability)
