I have the following 3d-numpy.ndarray:
import numpy as np
X = np.array([
[[0.0, 0.4, 0.6, 0.0, 0.0],
[0.6, 0.0, 0.0, 0.0, 0.0],
[0.4, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.6, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.4, 0.0, 1.0]],
[[0.1, 0.5, 0.4, 0.0, 0.0],
[0.6, 0.0, 0.0, 0.0, 0.0],
[0.2, 0.0, 0.0, 0.0, 0.0],
[0.1, 0.6, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.4, 0.0, 1.0]]
])
I want a new array where all the rows and columns are dropped where the diagnonal is equal to 1.
idx = np.diag(X[0]) == 1 # for my implementation it is sufficient to look at X[0]
Important to note is that X.shape[1] == X.shape[2], so I try to use the mask as follows
Y = X[:, ~idx, ~idx]
The above returns something different than my desired output:
[[0.0, 0.4, 0.6], [0.6, 0.0, 0.0], [0.4, 0.0, 0.0]], [[0.1, 0.5, 0.4], [0.6, 0.0, 0.0], [0.2, 0.0, 0.0]]
Please advice
Answer
Probably you can try
X[:, ~idx,:][:,:, ~idx]
or np.ix_
X[:, *np.ix_(~idx, ~idx)]
which gives
array([[[0. , 0.4, 0.6],
[0.6, 0. , 0. ],
[0.4, 0. , 0. ]],
[[0.1, 0.5, 0.4],
[0.6, 0. , 0. ],
[0.2, 0. , 0. ]]])