entropies_with_samples = []
for i in range(0,2948):
entr = entropy(predictProbas[i])
mixed = [proba_X_train[i],entr]
entropies_with_samples.append(mixed)
a = np.array(entropies_with_samples)
a.flatten("F")
print(list(chain.from_iterable(entropies_with_samples)))
selection = (sorted(mixed, key=itemgetter(2),reverse= True))
print(selection)
example:
input = [([0.2,0.10]),0.69, ([0.3,0.67]),0.70, ([0.5,0.68]),0.70, ([0.3,0.67]),0.65]
I'm trying to sort such an array at the third position.
output = [([0.3,0.67]),0.70, ([0.5,0.68]),0.70, ([0.2,0.10]),0.69, ([0.3,0.67]),0.65 ]
A first step could be to create a nested list, adding every 2
elements to a new sublist:
from itertools import chain
from operator import itemgetter
i = [([0.2,0.10]),0.69, ([0.3,0.67]),0.70, ([0.5,0.68]),0.70, ([0.3,0.67]),0.65]
l = [i[x:x+2] for x in range(0, len(i),2)]
# [[[0.2, 0.1], 0.69], [[0.3, 0.67], 0.7], [[0.5, 0.68], 0.7], [[0.3, 0.67], 0.65]]
And then sort the nested list by the second element in each sublist with operator.itemgetter
, and use itertools.chain
to flatten the result:
list(chain(*sorted(l, key = itemgetter(1), reverse=True)))
[[0.3, 0.67], 0.7, [0.5, 0.68], 0.7, [0.2, 0.1], 0.69, [0.3, 0.67], 0.65]
Another approach with zip and sorting with a lambda sorting key:
First, put your "third position" in a tuple with the first and second number, using zip:
output = list(zip(output[::2], output[1::2]))
#[([0.3, 0.67], 0.7), ([0.5, 0.68], 0.7), ([0.2, 0.1], 0.69), ([0.3, 0.67], 0.65)]
And then sort, using your third number (in the tuple it's on position 2) as the sorting key:
output.sort(key = lambda x: x[1])
#[([0.3, 0.67], 0.65), ([0.2, 0.1], 0.69), ([0.3, 0.67], 0.7), ([0.5, 0.68], 0.7)]
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