I am trying to find if there is a more efficient way of finding these combinations using some Python scientific library.
I am trying to avoid native for
loops and list append preferring to use some NumPy or similar functionality that in theory should be more efficient given it's using C code under the hood. I am struggling to find one, but to me this is quite a common problem to make these operations in an efficient way rather than using slow Python native structures.
I am wondering if I am looking in the wrong places? E.g. this does not seem to help here: https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.random.binomial.html
See here I am taking the binomial coefficients of a list of length 5 starting from a lower bound of 2 and finding out all the possible combinations. Meanwhile I append to a global list so I then have a nice list of "taken items" from the original input list.
import itertools
input_list = ['a', 'b', 'c', 'd', 'e']
minimum_amount = 2
comb_list = []
for i in range(minimum_amount, len(input_list)):
curr_list = input_list[:i+1]
print(f"the current index is: {i}, the lists are: {curr_list}")
curr_comb_list = list(itertools.combinations(curr_list, i))
comb_list = comb_list + curr_comb_list
print(f"found {len(comb_list)} combinations (check on set length: {len(set(comb_list))})")
print(comb_list)
Gives:
found 12 combinations (check on set length: 12)
[('a', 'b'), ('a', 'c'), ('b', 'c'), ('a', 'b', 'c'), ('a', 'b', 'd'),
('a', 'c', 'd'), ('b', 'c', 'd'), ('a', 'b', 'c', 'd'), ('a', 'b', 'c', 'e'),
('a', 'b', 'd', 'e'), ('a', 'c', 'd', 'e'), ('b', 'c', 'd', 'e')]
- Is it possible to do this avoiding the
for
loop and using some scientific libraries to do this quicker? - How can I do this in a quicker way?