The test function numpy.testing.assert_equal fails when comparing -0.0 and 0.0:
In [16]: np.testing.assert_equal(-0.0, 0.0)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-16-4063bd6da228> in <module>()
----> 1 np.testing.assert_equal(-0.0, 0.0)
/Users/warren/anaconda/lib/python2.7/site-packages/numpy/testing/utils.pyc
in assert_equal(actual, desired, err_msg, verbose)
309 elif desired == 0 and actual == 0:
310 if not signbit(desired) == signbit(actual):
--> 311 raise AssertionError(msg)
312 # If TypeError or ValueError raised while using isnan and
co, just handle
313 # as before
AssertionError:
Items are not equal:
ACTUAL: -0.0
DESIRED: 0.0
There is code that checks for this specific case, so this is
intentional. But this is not consistent with how negative zeros in
arrays are compared:
In [22]: np.testing.assert_equal(np.array(-0.0), np.array(0.0)) # PASS
In [23]: a = np.array([-0.0])
In [24]: b = np.array([0.0])
In [25]: np.testing.assert_array_equal(a, b) # PASS
Is there a reason the values are considered equal in an array, but not
when compared as scalars?
Warren
_______________________________________________
NumPy-Discussion mailing list
[email protected]
http://mail.scipy.org/mailman/listinfo/numpy-discussion
