That's most likely happening because of a np.nan
somewhere in the inputs involved. An example of it is shown below -
In [1]: A = np.array([4, 2, 1])
In [2]: B = np.array([2, 2, np.nan])
In [3]: A<=B
RuntimeWarning: invalid value encountered in less_equal
Out[3]: array([False, True, False], dtype=bool)
For all those comparisons involving np.nan
, it would output False
. Let's confirm it for a broadcasted
comparison. Here's a sample -
In [1]: A = np.array([4, 2, 1])
In [2]: B = np.array([2, 2, np.nan])
In [3]: A[:,None] <= B
RuntimeWarning: invalid value encountered in less_equal
Out[3]:
array([[False, False, False],
[ True, True, False],
[ True, True, False]], dtype=bool)
Please notice the third column in the output which corresponds to the comparison involving third element np.nan
in B
and that results in all False
values.
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