Description:

adaptivestratification 1.0.1

Adaptive Stratification
This package provides an implementation of the adaptive stratification sampling method to estimate quantities of interest of the form Q = E(f(Y)), where the random vector Y follows a d-dimensional uniform distribution on the unit-cube and f is a given function.
Example: Using the Sampler
# Import the module containing the sampling routines
from stratification import AdaptiveStratification

# Create a sampler for function func
sampler = AdaptiveStratification(func, d, N_max, N_new_per_stratum, alpha, type='hyperrect')

# Solve (return a tuple)
result = sampler.solve()

Input arguments:

func: implementation of given function of interest that defines the quantity of interest. It needs to be callable, accepting one m-times-n-dimensional numpy array as input and returns a m-dimensional numpy array;
d: dimension of the stochastic domain;
N_max: number of total samples to be used;
N_new_per_stratum: targeted average number of samples per stratum, controlling the adaptation;
alpha: number between zero and one, defining the hybrid allocation rule;
type: type of tessellation procedure, i.e., via hyper-rectangles (type='hyperrect') or simplices (type='simplex')

More Information
See the Github repository for more details.

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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