Numpy: random choice should support multidimensional arrays
random.choice enhancement request:
random.choice should support multidimensional arrays by taking an axis number. With this enhancement, axis=None would choose from a flattened array, while an integer argument would chose from the subarrays along that axis.
Apologies if this is a duplicate. My previous submission seems to have vanished.
alan-isaac
All 9 comments
This should be simple if you are thinking about doing it. All that seems necessary is to add a special check for axis=None and ravel in that case and replace make axis=1. Then set pop_size = a.shape[axis] and pass axis into take. (Plus possibly an error if a is just an int and axis is given).
Actually thinking about it, it could also be reasonable to have size=None as default and have it return a scalar/the given axes removed in that case. That would be a real change in functionality unfortunately, but it would make sense for similarity to import random; random.choice mostly.
One could think about also allowing an out argument which is just passed into take mostly. While none of the other random functions have that it could be a nice performance option in some cases.
seberg
on 12 Nov 2012
+1
samuela
on 23 Nov 2016
+1. Is this still not implemented?
jhollowed
on 28 Feb 2017
+1
jbachh
on 5 Mar 2018
Is this implemented?
slidenerd
on 22 Apr 2018
Is this ever going to be implemented?
mikesmith1611
on 30 Apr 2018
+1
JulianKlug
on 19 Jun 2018
+1
I also tried implementing this, before realizing that @masasin had already done it.
stephenpardy
on 11 Jul 2018
Implemented in the new randomgen API: np.random.Generator().choice(..., axis=int). Closing since we will not be updating the legacy code to support this. If you feel differently, please reopen for discussion.
mattip
on 29 May 2019
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Most helpful comment
+1. Is this still not implemented?