Slicing A Multidimensional Array Without A For Loop
I have a 3D array
r (1000 x 10 x 2000) constructed as follows:
q = np.random.normal(size=(10,2000)) r = np.random.normal(loc=q, size=(1000,10,2000))
r, can be viewed as a
1000 x 10 matrix repeated 2000 times.
I would like to reduce this array according to the following rule:
- from each matrix select only the column which has the max value in the first row
The columns to be selected ca be obtained via:
The result should be a
1000 x 2000 matrix.
I wonder if it is possible to get something like that without using a
for loop or list comprehensions.
Here is a
for loop which achieves the above task:
x =  for i, idx in enumerate(np.argmax(r, axis=0)): x.append(r[:,idx,i]) x = np.array(x).T
The solution I figured looks like this:
r[:, np.argmax(r,axis=0), np.arange(2000)]
More elegant solutions are, of course, welcome.
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