setup_oakley_function

pyapprox.benchmarks.setup_oakley_function()[source]

Setup the Oakely function benchmark

\[f(z) = a_1^Tz + a_2^T\sin(z) + a_3^T\cos(z) + z^TMz\]

where \(z\) consists of 15 I.I.D. standard Normal variables and the data \(a_1,a_2,a_3\) and \(~M\) are defined in the function get_oakley_function_data().

>>> from pyapprox.benchmarks.benchmarks import setup_benchmark
>>> benchmark=setup_benchmark('oakley')
>>> print(benchmark.keys())
dict_keys(['fun', 'variable', 'mean', 'variance', 'main_effects'])
Returns:
benchmarkBenchmark

Object containing the benchmark attributes

funcallable

The function being analyzed

variableJointVariable

Class containing information about each of the nvars inputs to fun

mean: np.ndarray (nvars)

The mean of the function with respect to the PDF of var

variance: np.ndarray (nvars)

The variance of the function with respect to the PDF of var

main_effectsnp.ndarray (nvars)

The variance based main effect sensitivity indices

References