Selective Grab Rows of Matrix Numpy
Selective Grab Rows of Matrix Numpy
Is there a way to index a 3D numpy matrix to selectively grab the ith row of each layer?
e.g. I have an RxNxR matrix, and I want to grab the 1st row of the 1st layer, the 2nd row of the 2nd layer, the 3rd row of the 3rd layer, etc. and end up with an RxN matrix.
Can I do that in a single operation?
3 Answers
3
Using the numpy.diagonal function:
a = np.arange(27).reshape(3, 3, 3)
np.diagonal(a, offset=0, axis1=0, axis2=1).T
gives
array([[ 0, 1, 2],
[12, 13, 14],
[24, 25, 26]])
It works if the matrix is 3x1x1... Which dimension corresponds to the layers? or you can try
np.diagonal(a, offset=0, axis1=0, axis2=2)– xdze2
Aug 23 at 15:22
np.diagonal(a, offset=0, axis1=0, axis2=2)
That did the trick! (the layers are the third dimension). Thanks!
– Vivek Sridhar
Aug 23 at 15:29
I believe this should do the trick:
import numpy as np
matrix = ...
result = np.zeros((matrix.shape[0], matrix.shape[1]))
for i in range(0, len(matrix)):
result[i] = matrix[i][i]
I think you could use a comprehension - depends on which dimensions you define as your 'layer', etc but this would work:
a = np.array([...])
[a[:, i, 0] for i in range(np.shape(a)[1])]
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can this possibly be generalized to also work with the case where R = 1? say I have a 1x3x1 matrix, this command is giving me only the first element, not the full row.
– Vivek Sridhar
Aug 23 at 15:16