import pyLHD
x = pyLHD.GoodLatticePoint(size = (11,10))
pyLHD.LqDistance(x).design()30.0maximin.maximinLHD
maximin.maximinLHD(size, h=None, method='LP', seed=None)
Generate a maximin LHD based on the L1-distance
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
size |
tuple of ints | Output shape of (n,d), where n and d are the number of rows and columns, respectively. |
required |
h |
list of ints | A generator vector used to multiply each row of the design. Each element in h must be smaller than and coprime to n |
None |
method |
Literal['LP', 'WT'] | Linear level permutation (LP) or William’s transformation (WT). Defaults to ‘LP’. | 'LP' |
seed |
Optional[Union[int, np.random.Generator]]) | If seedis an integer or None, a new numpy.random.Generator is created using np.random.default_rng(seed). If seed is already a `Generator instance, then the provided instance is used. Defaults to None. |
None |
Raises: ValueError: If method is not ‘LP’ or ‘WT’
Returns
| Type | Description |
|---|---|
| numpy.numpy.ndarray | A maximin LHD based on the L1-distance. Construction is obtained by applying Williams transformation on linearly permuted good lattice point (GLP) designs |
Example:
y = pyLHD.maximinLHD(size = (11,10), method = 'LP')
pyLHD.LqDistance(y).design()31.0w = pyLHD.maximinLHD(size = (11,10), method = 'WT')
pyLHD.LqDistance(w).design()39.0