rootStrategy = ot.MediumSafe(solver)
# %%
# Direction sampling algorithm.
# %%
samplingStrategy = ot.OrthogonalDirection()
# %%
# Create a simulation algorithm.
# %%
algo = ot.DirectionalSampling(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('Pf=', probability)
# %%
# We can observe the convergence history with the `drawProbabilityConvergence`
# method.
graph = algo.drawProbabilityConvergence()
graph.setLogScale(ot.GraphImplementation.LOGX)
assert result.getVarianceEstimate() == 0.0, "wrong var"
# case with last step threshold close from global threshold (before)
print('-' * 100)
event = ot.ThresholdEvent(Y, ot.Less(), -3.33832)
mc = ot.ProbabilitySimulationAlgorithm(event)
mc.run()
print('MC: ', mc.getResult())
ot.RandomGenerator.SetSeed(65132)
subset = ot.SubsetSampling(event)
subset.setMaximumOuterSampling(500)
subset.run()
print('SUBSET:', subset.getResult())
print('steps=', subset.getStepsNumber())
print('T=', subset.getThresholdPerStep())
# case with last step threshold close from global threshold (after)
print('-' * 100)
event = ot.ThresholdEvent(Y, ot.Less(), -3.338325)
mc = ot.ProbabilitySimulationAlgorithm(event)
mc.run()
print('MC: ', mc.getResult())
ot.RandomGenerator.SetSeed(65132)
subset = ot.SubsetSampling(event)
subset.setConvergenceStrategy(ot.Full())
subset.setMaximumOuterSampling(500)
subset.run()
print('SUBSET:', subset.getResult())
print('steps=', subset.getStepsNumber())
print('T=', subset.getThresholdPerStep())