numpy sum axis 1 in pure python-CodePudding

This may seem like a strange question, but how do you rewrite in pure python next line:

np.sum(three_dim_matrix, axis=1).cumsum(axis=1)

cumsum is supposed to be applied to a two-dimensional matrix, so the code for cumsum I could already find:

from itertools import accumulate
[list(accumulate(row)) for row in two_dim_matrix]

If you're really wondering why I don't use numpy, the problem is that optimizers for MINLP (such, GEKKO) don't support defining objective functions in numpy features

Example:

example = np.array([[[ 70,  110,  130],
                     [-50, -100, -200]],

                    [[300,  140,  120],
                     [300,  140,  120]],

                    [[ 400, 180, -240],
                     [1000, 320,  560]]])

first_step = np.sum(example, axis=1)
# [[  20   10  -70]
#  [ 600  280  240]
#  [1400  500  320]]

second_step = np.cumsum(first_step, axis=1)
# [[  20   30  -40]
#  [ 600  880 1120]
#  [1400 1900 2220]]

CodePudding user response：

In the two steps your example shows, data being the input list:

first_step = [list(map(sum, zip(*rows))) for rows in data]
second_step = [list(accumulate(row)) for row in first_step]

Or both steps combined (should be faster, as it doesn't build intermediate lists):

both_steps = [list(accumulate(map(sum, zip(*rows)))) for rows in data]

Try it online!

CodePudding user response：

You can solve this by looping into the structure at just the right level:

first_step = [
    [sum(col) for col in zip(*m)]
    for m in example
]

second_step = [list(accumulate(row)) for row in first_step]

You can also combine all this in one statement, similarly to Kelly Bundy's answer, by calling accumulate right on the rows of the intermediate matrix without actually building the intermediate matrix:

combined = [
    list(accumulate(sum(col) for col in zip(*m)))
    for m in example
]