I am having a numpy array of data
data = np.random.random((5, 5))
and another numpy array of equal shape masking the first one with classes from 0
to n
:
>>> mask
array([[3, 3, 1, 1, 0],
[2, 0, 1, 2, 2],
[0, 1, 0, 0, 3],
[2, 1, 1, 0, 2],
[0, 2, 3, 0, 2]])
What's the best way to compute a two-dimensional array with n
rows, where each row contains all elements from data
with class row_idx
(described by mask
)?
You can't do it better than O(n^2)
as you should iterate through all mask
array. As the number of elements in each class can be different the result can't be numpy array (rows have different sizes). So I don't think you can avoid python loop here with pure numpy functions. I suggest this O(n^2)
solution:
ans = [[] for i in range(mask.max() + 1)]
for k, v in np.ndenumerate(mask):
ans[v].append(data[k])
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