CODEWITHSUNDEEP
python-3.xnumpynumpy-ndarrayreduction
Kevin
Kevin

Reputation: 3348

Numpy reduce array to a given shape

I am trying to write a function that reduces a numpy ndarray to a given shape, which effectively "unbroadcasts" the array. For example, using add as the universal function, i want the following results:

This is the solution i came up with:

def unbroadcast(A, reduced_shape):
    fill = reduced_shape[-1] if reduced_shape else None
    reduced_axes = tuple(i for i, (a,b) in enumerate(itertools.zip_longest(A, shape, fillvalue=fill)) if a!=b)
    return np.add.reduce(A, axis=reduced_axes).reshape(shape)

But it feels unnecessarily complex, is there way to implement this that relies on Numpy's public API?

Upvotes: 0

Views: 441

Answers (2)

theo
theo

Reputation: 1

You can come up with a much more generic solution if you iterate from the right most dims. This is what numpy do when they broadcast: https://numpy.org/doc/stable/user/basics.broadcasting.html#general-broadcasting-rules.

just add itertools.zip_longest(reversed(list(A)), reversed(list(shape)))

Upvotes: -1

hpaulj
hpaulj

Reputation: 231335

It's not clear how this is an 'un-broadcasting'.

The straight forward way of doing your calculations is to use the axis parameter of sum:

In [124]: np.array([1,2,3,4,5]).sum()
Out[124]: 15
In [125]: np.array([[1,2],[1,2]]).sum(axis=0)
Out[125]: array([2, 4])
In [126]: np.array([[1,2],[1,2]]).sum(axis=(0,1))
Out[126]: 6
In [128]: np.array([[[1,2], [1,2]], [[1,2], [1,2]]]).sum(axis=0)
Out[128]: 
array([[2, 4],
       [2, 4]])

I don't use reduce as much, but looks like axis does the same:

In [130]: np.add.reduce(np.array([1,2,3,4,5]))
Out[130]: 15
In [132]: np.add.reduce(np.array([[[1,2], [1,2]], [[1,2], [1,2]]]),axis=0)
Out[132]: 
array([[2, 4],
       [2, 4]])

But I haven't worked out the logic of going from your reduced_shape to the necessary axis values. With shapes like (2,) and (2,2,2), there's potential ambiguity when you say reduce the shape to (2,2). It might be clearer if you worked with samples arrays like np.arange(24).reshape(2,3,4)

Upvotes: 1

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