Benchmark

class pyapprox.benchmarks.benchmarks.Benchmark[source]

Bases: scipy.optimize.optimize.OptimizeResult

Contains functions and results needed to implement known benchmarks.

The quantities

Notes

Use the keys() method to see a list of the available attributes for a specific benchmark

Attributes
funcallable

The function being analyzed

variablepya.variable

Class containing information about each of the nvars inputs to fun

jaccallable

The jacobian of fun. (optional)

hesscallable

The Hessian of fun. (optional)

hesspcallable

Function implementing the hessian of fun multiplied by a vector. (optional)

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

total_effectsnp.ndarray (nvars)

The variance based total effect sensitivity indices

sobol_indicesnp.ndarray

The variance based Sobol sensitivity indices