# commutative allclose

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## commutative allclose

 Should allclose() be commutative, so as to prevent the following: >>>  x = 1.00001001 >>> allclose(x,1), allclose(1,x) (False, True) There is some discussion here which provides two possible solutions: http://www.boost.org/doc/libs/1_35_0/libs/test/doc/components/test_tools/floating_point_comparison.htmlNotice, the discussion states that their solutions are not transitive---nevertheless, I think commutativity is a worthwhile improvement.  Also, they mention that rtol * abs(y) can cause underflow issues.  Thus, they implement, |x-y|/|y| <= rtol     AND(OR)     |x-y|/|x| <= rtol without an atol option, rather than |x-y| <= atol + rtol* |y| Naively, it seems like atol is attempting to correct this very issue. So do we really need atol? _______________________________________________ Numpy-discussion mailing list [hidden email] http://projects.scipy.org/mailman/listinfo/numpy-discussion
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## Re: commutative allclose

 On Thu, Apr 10, 2008 at 1:56 PM, Tom Johnson <[hidden email]> wrote: Should allclose() be commutative, so as to prevent the following: >>>  x = 1.00001001 >>> allclose(x,1), allclose(1,x) (False, True) There is some discussion here which provides two possible solutions: http://www.boost.org/doc/libs/1_35_0/libs/test/doc/components/test_tools/floating_point_comparison.html Notice, the discussion states that their solutions are not transitive---nevertheless, I think commutativity is a worthwhile improvement.  Also, they mention that rtol * abs(y) can cause underflow issues.  Thus, they implement, |x-y|/|y| <= rtol     AND(OR)     |x-y|/|x| <= rtol without an atol option, rather than |x-y| <= atol + rtol* |y| Naively, it seems like atol is attempting to correct this very issue. So do we really need atol?I think it useful, especially when working with values that may be zero. However, it might not be necessary in the allclose context. On the other point, it is also possible to use the max, avg, or sum of |x| and |y|, which will avoid the case when on of them gets too small. Chuck _______________________________________________ Numpy-discussion mailing list [hidden email] http://projects.scipy.org/mailman/listinfo/numpy-discussion