sampling.lhs.rlh

sampling.lhs.rlh(n, k, edges=0, seed=None)

Generates a random Latin hypercube within the [0,1]^k hypercube.

Parameters

Name	Type	Description	Default
n	int	Desired number of points.	required
k	int	Number of design variables (dimensions).	required
edges	int	If 1, places centers of the extreme bins at the domain edges ([0,1]). Otherwise, bins are fully contained within the domain, i.e. midpoints. Defaults to 0.	0

Returns

Name	Type	Description
	np.ndarray	np.ndarray: A Latin hypercube sampling plan of n points in k dimensions, with each coordinate in the range [0,1].

Examples

>>> from spotoptim.sampling.lhs import rlh
>>> import numpy as np
>>> # Generate a 2D Latin hypercube with 5 points and edges=0
>>> X = rlh(n=5, k=2, edges=0)
>>> print(X)
# Example output (values vary due to randomness):
# [[0.1  0.5 ]
#  [0.7  0.1 ]
#  [0.9  0.7 ]
#  [0.3  0.9 ]
#  [0.5  0.3 ]]