I'd like to MinMax normalize the following 3D numpy array "at 2D-layer level" :
np.array([[[0, 1, 2],
[3, 4, 5],
[6, 7, 8]],
[[0, 1, 2],
[3, 4, 5],
[6, 7, 10]],
[[0, 1, 2],
[3, 4, 5],
[6, 7, 12]]])
to obtain :
np.array([[[0. , 0.1, 0.2],
[0.3, 0.4, 0.5],
[0.6, 0.7, 1. ]],
[[0. , 0.1, 0.2],
[0.3, 0.4, 0.5],
[0.6, 0.7, 1. ]],
[[0. , 0.08333333, 0.16666667],
[0.25 , 0.33333333, 0.41666667],
[0.5 , 0.58333333, 1. ]]])
any idea how if could be done without using loops ? Many thanks in advance !
2 Answers
One approach is to use .max as follows:
res = arr / arr.max(axis=(1, 2), keepdims=True)
print(res)
Output
[[[0.125 0.125 0.25 ]
[0.375 0.5 0.625 ]
[0.75 0.875 1. ]]
[[0. 0.1 0.2 ]
[0.3 0.4 0.5 ]
[0.6 0.7 1. ]]
[[0. 0.08333333 0.16666667]
[0.25 0.33333333 0.41666667]
[0.5 0.58333333 1. ]]]
If you define your array as:
x = np.array([[[0, 1, 2],
[3, 4, 5],
[6, 7, 8]],
[[0, 1, 2],
[3, 4, 5],
[6, 7, 10]],
[[0, 1, 2],
[3, 4, 5],
[6, 7, 12]]])
You can use reshape to flatten the array:
(
x.reshape(x.shape[-1],x.shape[0]*x.shape[1]).T /
np.max(x.reshape(x.shape[2], x.shape[0]*x.shape[1]), axis=1)
).T.reshape(x.shape)
Here the array is flatten to a 2D array where one can take the max of axis=1.
