SALib.sample.radial.radial_funcs module

SALib.sample.radial.radial_funcs.combine_samples(baseline, perturb)

Combine baseline and perturbation samples in order.

The number of parameters will be inferred from the shape of baseline.

Parameters:

• baseline (np.array) – baseline sample for one of N samples

• perturb (np.array) – perturbation value for a parameter

Example

>>> X = combine_samples(baseline, perturb)

X will now hold:

[[x_{1,1}, x_{1,2}, …, x_{1,p}] [b_{1,1}, x_{1,2}, …, x_{1,p}] [x_{1,1}, b_{1,2}, …, x_{1,p}] [x_{1,1}, x_{1,2}, …, b_{1,p}] … [x_{N,1}, x_{N,2}, …, x_{N,p}] [b_{N,1}, x_{N,2}, …, x_{N,p}] [x_{N,1}, b_{N,2}, …, x_{N,p}] [x_{N,1}, x_{N,2}, …, b_{N,p}]]

where p denotes the number of parameters as specified in problem and N is the number of samples.

We can now run the model using the values in X. The total number of model evaluations will be N(p+1).

Return type:

np.array