[Numpy-discussion] Broadcasting with np.logical_and.reduce

Robert Kern robert.kern at gmail.com
Fri Sep 12 05:22:06 EDT 2014


On Fri, Sep 12, 2014 at 10:04 AM, Antony Lee <antony.lee at berkeley.edu> wrote:
> I am not using asarray here.  Sorry, but I don't see how this is relevant --
> my comparison with np.add.reduce is simply that when a list of float arrays
> is passed to np.add.reduce, broadcasting happens as usual, but not when a
> list of bool arrays is passed to np.logical_and.reduce.

But np.logical_and.reduce() *does* use asarray() when it is given a
list object (all ufunc .reduce() methods do this). In both cases, you
get a dtype=object array. This means that the ufunc will use the
dtype=object inner loop, not the dtype=bool inner loop. For np.add,
this isn't a problem. It just calls the __add__() method on the first
object which, since it's an ndarray, calls np.add() again to do the
actual work, this time using the appropriate dtype inner loop for the
inner objects. But np.logical_and is different! For the dtype=object
inner loop, it directly calls bool(x) on each item of the object
array; it doesn't defer to any other method that might do the
computation. bool(almost_any_ndarray) raises the ValueError that you
saw. np.logical_and.reduce([x, y]) is not the same as
np.logical_and(x, y). You can see how the dtype=object inner loop of
np.logical_and() works by directly constructing dtype=object shape-()
arrays:

[~]
|14> x
array(None, dtype=object)

[~]
|15> x[()] = np.array([True, False])

[~]
|16> x
array(array([ True, False], dtype=bool), dtype=object)

[~]
|17> y = np.array(None, dtype=object)

[~]
|18> y[()] = np.array([[True], [False]])

[~]
|19> y
array(array([[ True],
       [False]], dtype=bool), dtype=object)

[~]
|20> np.logical_and(x, y)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-20-17705aa17a6f> in <module>()
----> 1 np.logical_and(x, y)

ValueError: The truth value of an array with more than one element is
ambiguous. Use a.any() or a.all()

-- 
Robert Kern



More information about the NumPy-Discussion mailing list