def subset_event(ev):
algo = ot.SubsetSampling(ev)
algo.setMaximumOuterSampling(2500)
algo.setBlockSize(4)
algo.run()
result = algo.getResult()
return result.getProbabilityEstimate()
def run_SubSet(
event,
coefVar=0.05,
outerSampling=1000,
seed=1234,
verbose=False,
failure_domain=None,
):
"""
Run a Subset simulation
"""
# Initialize the random generator
ot.RandomGenerator.SetSeed(seed)
# Run LHS simulation
simulation = ot.SubsetSampling(event, 2.0, 0.05)
simulation.setMaximumCoefficientOfVariation(coefVar)
simulation.setMaximumOuterSampling(outerSampling)
# simulation.setBlockSize(blockSize)
# try:
simulation.run()
# except Exception as e:
# dump_cache(model, 'Cache/physicalModelMathFunction')
# raise e
result = simulation.getResult()
dfResult = pd.DataFrame()
dfResult = dfResult.append(
pd.DataFrame([result.getProbabilityEstimate()],
index=["Probability of failure"]))
dfResult = dfResult.append(
pd.DataFrame(
[result.getCoefficientOfVariation()],
index=["Coefficient of varation"],
))
dfResult = dfResult.append(
pd.DataFrame([result.getConfidenceLength()],
index=["95 % Confidence length"]))
dfResult = dfResult.append(
pd.DataFrame(
[result.getOuterSampling() * result.getBlockSize()],
index=["Number of calls"],
))
dfResult = dfResult.reset_index()
dfResult.columns = ["", "Results - Subset"]
if verbose:
print(dfResult, "\n")
return simulation
# %%
Y = ot.CompositeRandomVector(g, X)
# %%
# Create the event :math:`\{ Y = g(X) \leq 0 \}`
# ----------------------------------------------
# %%
myEvent = ot.ThresholdEvent(Y, ot.LessOrEqual(), 0.0)
# %%
# Evaluate the probability with the subset sampling technique
# -----------------------------------------------------------
# %%
algo = ot.SubsetSampling(myEvent)
# %%
# In order to get all the inputs and outputs that realize the event, you have to mention it now:
# %%
algo.setKeepEventSample(True)
# %%
# Now you can run the algorithm!
# %%
algo.run()
# %%
result = algo.getResult()
# # Computation # bs = 1 # Monte Carlo experiment = ot.MonteCarloExperiment() myMC = ot.ProbabilitySimulationAlgorithm(myEvent, experiment) myMC.setMaximumOuterSampling(int(1e6) // bs) myMC.setBlockSize(bs) myMC.setMaximumCoefficientOfVariation(-1.0) myMC.run() # # SubsetSampling mySS = ot.SubsetSampling(myEvent) mySS.setMaximumOuterSampling(10000 // bs) mySS.setBlockSize(bs) mySS.run() # # Results # # Monte Carlo resultMC = myMC.getResult() PFMC = resultMC.getProbabilityEstimate() CVMC = resultMC.getCoefficientOfVariation() variance_PF_MC = resultMC.getVarianceEstimate() length90MC = resultMC.getConfidenceLength(0.90) N_MC = resultMC.getOuterSampling() * resultMC.getBlockSize()
# # Computation # bs = 1 # Monte Carlo experiment = ot.MonteCarloExperiment() myMC = ot.ProbabilitySimulationAlgorithm(myEvent, experiment) myMC.setMaximumOuterSampling(int(1e6) // bs) myMC.setBlockSize(bs) myMC.setMaximumCoefficientOfVariation(-1.0) myMC.run() # # SubsetSampling mySS = ot.SubsetSampling(myEvent) mySS.setMaximumOuterSampling(10000 // bs) mySS.setBlockSize(bs) mySS.setKeepEventSample(True) mySS.run() # # Results # # Monte Carlo resultMC = myMC.getResult() PFMC = resultMC.getProbabilityEstimate() CVMC = resultMC.getCoefficientOfVariation() variance_PF_MC = resultMC.getVarianceEstimate() length90MC = resultMC.getConfidenceLength(0.90)
