Print a reliability benchmark problem

import otbenchmark as otb

problem = otb.RminusSReliability()
print(problem)

name = R-S
event = class=ThresholdEventImplementation antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[R,S,y0] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[R,S] outputVariablesNames=[y0] formulas=[R - S] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[R,S] outputVariablesNames=[y0] formulas=[R - S] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[R,S] outputVariablesNames=[y0] formulas=[R - S] antecedent=class=UsualRandomVector distribution=class=JointDistribution name=JointDistribution dimension=2 copula=class=IndependentCopula name=IndependentCopula dimension=2 marginal[0]=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[4] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] marginal[1]=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[2] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] operator=class=Less name=Unnamed threshold=0
probability = 0.07864960352514257

print(problem.toFullString())

name = R-S
function = [R,S]->[R - S]
operator = <
threshold = 0.0
probability = 0.07864960352514257
beta = 1.414213562373095
distribution = JointDistribution(Normal(mu = 4, sigma = 1), Normal(mu = 2, sigma = 1), IndependentCopula(dimension = 2))

problem.getName()

'R-S'

event = problem.getEvent()

g = event.getFunction()
g

[R,S]->[R - S]

event.getOperator()

<

threshold = event.getThreshold()
threshold

0.0

pf = problem.getProbability()
pf

0.07864960352514257

beta = problem.computeBeta()
beta

1.414213562373095

inputVector = event.getAntecedent()
distribution = inputVector.getDistribution()
distribution

JointDistribution

• name=JointDistribution
• dimension: 2
• description=[R,S]
• copula: IndependentCopula(dimension = 2)

Index	Variable	Distribution
0	R	Normal(mu = 4, sigma = 1)
1	S	Normal(mu = 2, sigma = 1)