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For the purpose of conducting a psychological experiment I have to divide a set of pictures (240) described by 4 features (real numbers) into 3 subsets with equal number of elements in each subset (240/3 = 80) in such a way that all subsets are approximately balanced with respect to these features (in terms of mean and standard deviation).
Can anybody suggest an algorithm to automate that? Are there any packages/modules in Python or R that I could use to do that? Where should I start?
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If number of subsets whose sum reaches the required sum is (K-1), we flag that it is possible to partition array into K parts with equal sum, because remaining elements already have a sum equal to required sum.
To partition nums , put each element of nums into one of the two arrays. Return the minimum possible absolute difference. Input: nums = [3,9,7,3] Output: 2 Explanation: One optimal partition is: [3,9] and [7,3]. The absolute difference between the sums of the arrays is abs((3 + 9) - (7 + 3)) = 2.
If I understand correctly your problem, you might use random.sample() in python:
import random
pool = set(["foo", "bar", "baz", "123", "456", "789"]) # your 240 elements here
slen = len(pool) / 3 # we need 3 subsets
set1 = set(random.sample(pool, slen)) # 1st random subset
pool -= set1
set2 = set(random.sample(pool, slen)) # 2nd random subset
pool -= set2
set3 = pool # 3rd random subset
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