You can use itertools.combinations()
to create the index array, and then use NumPy’s fancy indexing:
import numpy as np
from itertools import combinations, chain
from scipy.special import comb
def comb_index(n, k):
count = comb(n, k, exact=True)
index = np.fromiter(chain.from_iterable(combinations(range(n), k)),
int, count=count*k)
return index.reshape(-1, k)
data = np.array([[1,2,3,4,5],[10,11,12,13,14]])
idx = comb_index(5, 3)
print(data[:, idx])
output:
[[[ 1 2 3]
[ 1 2 4]
[ 1 2 5]
[ 1 3 4]
[ 1 3 5]
[ 1 4 5]
[ 2 3 4]
[ 2 3 5]
[ 2 4 5]
[ 3 4 5]]
[[10 11 12]
[10 11 13]
[10 11 14]
[10 12 13]
[10 12 14]
[10 13 14]
[11 12 13]
[11 12 14]
[11 13 14]
[12 13 14]]]