Benchmark¶
-
class
pyapprox.benchmarks.benchmarks.Benchmark[source]¶ Bases:
scipy.optimize.optimize.OptimizeResultContains 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