SparsePolynomialChaosSensitivityAnalysis

class SparsePolynomialChaosSensitivityAnalysis(sensitivityBenchmarkProblem, sample_size_train=100, sample_size_test=100, total_degree=2, hyperbolic_quasinorm=0.5)

Methods

run([verbose])

Estimate the sensitivity indices from chaos.

__init__(sensitivityBenchmarkProblem, sample_size_train=100, sample_size_test=100, total_degree=2, hyperbolic_quasinorm=0.5)

Estimate Sobol’ sensitivity indices from sparse polynomial chaos.

Uses regression to estimate the coefficients. Uses LARS to select the model. Uses hyperbolic enumerate rule. Uses Sobol’ low discrepancy sequence to train the polynomial. Uses Monte-Carlo sample to test the polynomial.

Parameters:

sensitivityBenchmarkProblemotb.SensitivityBenchmarkProblem

The problem.

sample_size_trainint, optional

The training sample size. The default is 100.

sample_size_testint, optional

The test sample size. The default is 100.

total_degreeint, optional

The total polynomial degree. The default is 2.

hyperbolic_quasinormfloat, optional

The hyperbolic quasi-norm. The default is 0.5.

Returns:

None.

run(verbose=False)

Estimate the sensitivity indices from chaos.

Parameters:

verbosebool, optional

If True, print intermediate messages. The default is False.

Returns:

resultotb.SparsePolynomialChaosSensitivityResult

The result of the calculation.