[Numpy-discussion] Ill-defined in-place operations (#1085)
Pauli Virtanen
pav@iki...
Wed Apr 15 14:52:54 CDT 2009
Mixing views and mutating (eg. in-place) operations can cause surprising
and ill-defined results. Consider
http://projects.scipy.org/numpy/ticket/1085:
>>> import numpy as np
>>> x = np.array([[1,2], [3,4]])
>>> x
array([[1, 2],
[3, 4]])
>>> x += x.T
>>> x
array([[2, 5],
[8, 8]])
>>> y = np.array([[1,2], [3,4]], order='F')
>>> y
array([[1, 2],
[3, 4]])
>>> y += y.T
>>> y
array([[2, 7],
[5, 8]])
The result depends on the order in which the elements happen to lie in
the memory. Predicting the outcome is nearly impossible. (Also, I think
Numpy tries to optimize the order of the loops, making it even more
difficult?)
This is a sort of a pitfall. Should Numpy issue a warning every time a
mutating operation is performed on an array, with input data that is a
view on the same array?
Some alternatives:
a) Raise warning for all arrays, even 1-D.
b) Raise warning only for multidimensional arrays. (Assume the user can
predict what happens in 1-D.)
c) Don't raise any warnings, just educate users in the documentation.
This is not the first time this has been discussed on this ML, but the
last discussion did not (IIRC) lead to any conclusions. (But I think it
was established that detecting when views really overlap is a problem
with no cheap solution. So walking up ->base pointers or comparing ->data
pointers seemed nearly the only feasible way.)
--
Pauli Virtanen
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