romcomma.user.sample.DOE§

class DOE[source]§

Bases: object

Sampling methods for inputs.

__init__()§

Methods

__init__()

full_factorial(N, M)

Full factorial DOE.

latin_hypercube(N, M[, is_centered])

Latin Hypercube DOE.

space_filling_test(X, o)

Test whether X is a space-filling design matrix, by finding the distance to the nearest point in X for o test points.

Attributes

Method

Function signature of a DOE method.
Method§

Function signature of a DOE method.

alias of Callable[[int, int, Any], ndarray]

static latin_hypercube(N, M, is_centered=True, **kwargs)[source]§

Latin Hypercube DOE.

Parameters:

  • N (int) – The number of samples (rows).

  • M (int) – The of input dimensions (columns).

  • is_centered (bool) – Boolean ordinate whether to centre each sample in its Latin Hypercube cell. Default is False, which locates the sample randomly within its cell.

  • kwargs – Passed directly to scipy.stats.qmc.LatinHypercube.

Returns: An (N,M) matrix of N samples of dimension M.

static full_factorial(N, M)[source]§

Full factorial DOE.

Parameters:

  • N (int) – The number of samples (rows).

  • M (int) – The of input dimensions (columns).

Returns: An (N,M) matrix of N samples of dimension M.

static space_filling_test(X, o)[source]§

Test whether X is a space-filling design matrix, by finding the distance to the nearest point in X for o test points.

Parameters:

  • X (ndarray) – An (N,M) design matrix.

  • o (int) – The number of test points used to assess whether X is a space-filling design.

Return type:

Dict[str, float]

Returns: A dict of six measures: The theoretical hard upper bound, expected upper bound and expected lower bound for a perfectly space-filling

design matrix, followed by the max, mean and SD of the distance-to-nearest-in-X over the o test points.