otbenchmark
ReliabilityBenchmarkMetaAlgorithm¶
- class ReliabilityBenchmarkMetaAlgorithm(problem)¶
Methods
runFORM(nearestPointAlgorithm)Runs the FORM algorithm and get the results.
runFORMImportanceSampling(nearestPointAlgorithm)Runs the Importance Sampling method with FORM importance distribution and get the number of function evaluations.
runLHS([maximumOuterSampling, ...])Runs the LHS algorithm and get the results.
runMonteCarlo([maximumOuterSampling, ...])Runs the ProbabilitySimulationAlgorithm with Monte-Carlo experiment algorithm and get results.
runSORM(nearestPointAlgorithm)Runs the SORM algorithm and get the results.
runSubsetSampling([maximumOuterSampling, ...])Runs the Subset method and get the results.
- __init__(problem)¶
Create a meta-algorithm to solve a reliability problem.
- Parameters:
- problemot.ReliabilityBenchmarkProblem
The problem.
- runFORM(nearestPointAlgorithm)¶
Runs the FORM algorithm and get the results.
- Parameters:
- nearestPointAlgorithmot.OptimizationAlgorithm
Optimization algorithm used to search the design point.
- Returns:
- resultReliabilityBenchmarkResult
The problem result.
- runFORMImportanceSampling(nearestPointAlgorithm, maximumOuterSampling=1000, coefficientOfVariation=0.1, blockSize=1)¶
Runs the Importance Sampling method with FORM importance distribution and get the number of function evaluations.
- Parameters:
- nearestPointAlgorithmot.OptimizationAlgorithm
Optimization algorithm used to search the design point.
- maximumOuterSamplingint
The maximum number of outer iterations.
- coefficientOfVariationfloat
The maximum coefficient of variation.
- blockSizeint
The number of inner iterations.
- Returns:
- resultReliabilityBenchmarkResult
The problem result.
- runLHS(maximumOuterSampling=1000, coefficientOfVariation=0.1, blockSize=1)¶
Runs the LHS algorithm and get the results.
- Parameters:
- maximumOuterSamplingint
The maximum number of outer iterations.
- coefficientOfVariationfloat
The maximum coefficient of variation.
- blockSizeint
The number of inner iterations.
- Returns:
- resultReliabilityBenchmarkResult
The problem result.
- runMonteCarlo(maximumOuterSampling=1000, coefficientOfVariation=0.1, blockSize=1)¶
Runs the ProbabilitySimulationAlgorithm with Monte-Carlo experiment algorithm and get results.
- Parameters:
- maximumOuterSamplingint
The maximum number of outer iterations.
- coefficientOfVariationfloat
The maximum coefficient of variation.
- blockSizeint
The number of inner iterations.
- Returns:
- resultReliabilityBenchmarkResult
The problem result.
- runSORM(nearestPointAlgorithm)¶
Runs the SORM algorithm and get the results.
- Parameters:
- nearestPointAlgorithmot.OptimizationAlgorithm
Optimization algorithm used to search the design point.
- Returns:
- resultReliabilityBenchmarkResult
The problem result.
- runSubsetSampling(maximumOuterSampling=1000, coefficientOfVariation=0.1, blockSize=1)¶
Runs the Subset method and get the results.
- Parameters:
- maximumOuterSamplingint
The maximum number of outer iterations.
- coefficientOfVariationfloat
The maximum coefficient of variation.
- blockSizeint
The number of inner iterations.
- Returns:
- resultReliabilityBenchmarkResult
The problem result.