I have two arrays by shape c = (2, 10, 2, 3) and p = (2, 5, 3). I want to pick the first vectors in the 3d dimension of c and minus it from each vectors in the corresponding row of p. It will be better understandable by this example, we can do this operation on the first row by c[0, :, None, 0] - p[0] or c[0, :, 0][:, None] - p[0], which will get result with shape (10, 5, 3). But I have confused how to apply this operation on all rows at once by advanced indexing, which result will be as shape (2, 10, 5, 3).
p = np.array([[[0., -0.05, 0.], [0., -0.05, 0.], [0., 0., 0.], [-0.05, -0.1, 0.05], [0.1, 0.2, 0.1]],
[[0., -0.05, 0.], [0., -0.05, 0.], [0., 0., 0.], [-0.05, -0.1, 0.05], [0.1, 0.2, 0.1]]])
c = np.array([[[[ 0.05, 0. , 0.05], [ 0.05, -0.1, 0.05]], [[ 0.05, -0.1, 0.05], [-0.05, -0.1, 0.05]],
[[-0.05, -0.1, 0.05], [-0.05, 0. , 0.05]], [[ 0.05, 0. , -0.05], [ 0.05, -0.1, -0.05]],
[[ 0.05, -0.1, -0.05], [-0.05, -0.1, -0.05]], [[-0.05, -0.1, -0.05], [-0.05, 0. , -0.05]],
[[ 0.05, 0. , 0.05], [ 0.05, 0. , -0.05]], [[ 0.05, -0.1, 0.05], [ 0.05, -0.1, -0.05]],
[[-0.05, -0.1, 0.05], [-0.05, -0.1, -0.05]], [[-0.05, 0. , 0.05], [-0.05, 0. , -0.05]]],
[[[ 0.05, 0.1, 0.05], [ 0.05, 0. , 0.05]], [[ 0.05, 0. , 0.05], [-0.05, 0. , 0.05]],
[[-0.05, 0. , 0.05], [-0.05, 0.1, 0.05]], [[ 0.05, 0.1, -0.05], [ 0.05, 0. , -0.05]],
[[ 0.05, 0. , -0.05], [-0.05, 0. , -0.05]], [[-0.05, 0. , -0.05], [-0.05, 0.1, -0.05]],
[[ 0.05, 0.1, 0.05], [ 0.05, 0.1, -0.05]], [[ 0.05, 0. , 0.05], [ 0.05, 0. , -0.05]],
[[-0.05, 0. , 0.05], [-0.05, 0. , -0.05]], [[-0.05, 0.1, 0.05], [-0.05, 0.1, -0.05]]]])
additional suggestion:
It is not the main issue, but will be helpful for me and I will be grateful for any alternative way to handle this:
I have broadcasted p from shape (5, 3) to (2, 5, 3) to be corresponded with 1st dimension of c. Is there a better way, without this broadcasting (may be just by advanced indexing instead), to handle this problem?
Let's start here:
I have broadcasted p from shape (5, 3) to (2, 5, 3) to be corresponded with 1st dimension of c. Is there a better way, without this broadcasting (may be just by advanced indexing instead), to handle this problem?
So, if I understand correctly, p should actually be (5, 3), you doubled it manually to make a (2, 5, 3) array. As you'll see, you do not have to do that, actually sticking to a (5, 3) array is going to make the whole thing much easier.
Now when you do this:
c[0, :, None, 0]
What you get is a (10, 1, 3) array. You're on the right track here, as the second dimension of 1 is what you'll need for broadcasting, but again, you can do even simpler with
c[:, :, None, 0]
which will give you a (2, 10, 1, 3) array. Which is something you really much need in order to take advantage of numpy's broadcasting rules:
When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing (i.e. rightmost) dimensions and works its way left. Two dimensions are compatible when: 1. they are equal, or 2. one of them is 1
So, if we have p.shape == (5, 3) and c transformed so that c.shape == (2, 10, 1, 3), the matching will go right from left:
Schematically:
(2, 10, 1, 3)
| | | |
| | V V
| | (5, 3)
| | | |
V V V V
(2, 10, 5, 3)
Long explanation but actually the application is going to be very simple. So, let's try it:
import numpy as np
# let's keep this one at (5, 3)
p = np.array([[0., -0.05, 0.], [0., -0.05, 0.], [0., 0., 0.], [-0.05, -0.1, 0.05], [0.1, 0.2, 0.1]])
c = np.array([[[[ 0.05, 0. , 0.05], [ 0.05, -0.1, 0.05]], [[ 0.05, -0.1, 0.05], [-0.05, -0.1, 0.05]],
[[-0.05, -0.1, 0.05], [-0.05, 0. , 0.05]], [[ 0.05, 0. , -0.05], [ 0.05, -0.1, -0.05]],
[[ 0.05, -0.1, -0.05], [-0.05, -0.1, -0.05]], [[-0.05, -0.1, -0.05], [-0.05, 0. , -0.05]],
[[ 0.05, 0. , 0.05], [ 0.05, 0. , -0.05]], [[ 0.05, -0.1, 0.05], [ 0.05, -0.1, -0.05]],
[[-0.05, -0.1, 0.05], [-0.05, -0.1, -0.05]], [[-0.05, 0. , 0.05], [-0.05, 0. , -0.05]]],
[[[ 0.05, 0.1, 0.05], [ 0.05, 0. , 0.05]], [[ 0.05, 0. , 0.05], [-0.05, 0. , 0.05]],
[[-0.05, 0. , 0.05], [-0.05, 0.1, 0.05]], [[ 0.05, 0.1, -0.05], [ 0.05, 0. , -0.05]],
[[ 0.05, 0. , -0.05], [-0.05, 0. , -0.05]], [[-0.05, 0. , -0.05], [-0.05, 0.1, -0.05]],
[[ 0.05, 0.1, 0.05], [ 0.05, 0.1, -0.05]], [[ 0.05, 0. , 0.05], [ 0.05, 0. , -0.05]],
[[-0.05, 0. , 0.05], [-0.05, 0. , -0.05]], [[-0.05, 0.1, 0.05], [-0.05, 0.1, -0.05]]]])
# now, it's really as simple as that
result = c[:, :, None, 0] - p # shape: (2, 10, 5, 3)