On 17 December 2015 at 14:43, Nico Schlömer <nico.schloe...@gmail.com> wrote:
> I'm not sure where I'm going wrong here. Any hints? You are dancing around the boundary between close floating point numbers, and when you are dealing with ULPs, number of decimal places is a bad measure. Working with plain numbers, instead of arrays (just so that the numbers are printed in full detail) a1 = np.float64(3.1415926535897930) a2 = np.float64(3.1415926535897939) They are numerically different: a2 - a1 8.8817841970012523e-16 In epsilons (defined as the smallest number such that (1 + eps) - 1 > 0): (a2 - a1) / np.finfo(np.float64).eps 4.0 In fact, there is one number in between, two epsilons away from each one: np.nextafter(a1, a2) 3.1415926535897936 np.nextafter(np.nextafter(a1, 10), 10) - a2 0.0 The next number on the other side: np.nextafter(a1, 0) 3.1415926535897927 For more information: print np.finfo(np.float64) Machine parameters for float64 --------------------------------------------------------------- precision= 15 resolution= 1.0000000000000001e-15 machep= -52 eps= 2.2204460492503131e-16 negep = -53 epsneg= 1.1102230246251565e-16 minexp= -1022 tiny= 2.2250738585072014e-308 maxexp= 1024 max= 1.7976931348623157e+308 nexp = 11 min= -max --------------------------------------------------------------- /David
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