helpers.axis_combinations

helpers.axis_combinations(arr, k, axis=0)

Generates all unique combinations of columns from the given array, selecting ‘k’ columns at a time.

Parameters

Name	Type	Description	Default
arr	numpy.numpy.ArrayLike	A numpy ndarray	required
k	int	The number of elements to include in each combination	required
axis	int	Specified axis to obtain combinations. axis = 0 for row combinations, axis = 1 for column combinations. Defaults to 0.	0

Returns: List[npt.ArrayLike]: A list of arrays, each being a combination of ‘k’ elements from the original array. The combinations are returned as slices of the original array, not copies.

Examples:

import pyLHD
random_ls = pyLHD.LatinSquare(size = (4,4),seed = 1)
random_ls

array([[2, 3, 1, 2],
       [4, 2, 4, 4],
       [1, 4, 3, 3],
       [3, 1, 2, 1]])

Obtain all 2 column combinations of random_ls

pyLHD.axis_combinations(random_ls, k = 2, axis = 1)

[array([[2, 3],
        [4, 2],
        [1, 4],
        [3, 1]]),
 array([[2, 1],
        [4, 4],
        [1, 3],
        [3, 2]]),
 array([[2, 2],
        [4, 4],
        [1, 3],
        [3, 1]]),
 array([[3, 1],
        [2, 4],
        [4, 3],
        [1, 2]]),
 array([[3, 2],
        [2, 4],
        [4, 3],
        [1, 1]]),
 array([[1, 2],
        [4, 4],
        [3, 3],
        [2, 1]])]

Obtain all 2 row combinations of random_ls

pyLHD.axis_combinations(random_ls, k = 2, axis = 0)

[array([[2, 3, 1, 2],
        [4, 2, 4, 4]]),
 array([[2, 3, 1, 2],
        [1, 4, 3, 3]]),
 array([[2, 3, 1, 2],
        [3, 1, 2, 1]]),
 array([[4, 2, 4, 4],
        [1, 4, 3, 3]]),
 array([[4, 2, 4, 4],
        [3, 1, 2, 1]]),
 array([[1, 4, 3, 3],
        [3, 1, 2, 1]])]