[Python-ideas] Way to check for floating point "closeness"?
steve at pearwood.info
Wed Jan 14 11:39:20 CET 2015
On Tue, Jan 13, 2015 at 08:57:38PM -0600, Ron Adam wrote:
> On 01/13/2015 09:53 AM, Chris Barker - NOAA Federal wrote:
> >I haven't thought it out yet, but maybe we could specify an absolute
> >tolerance near zero, and a relative tolerance elsewhere, both at once.
> >Tricky to document, even if possible.
> Doesn't this problem come up at any boundary comparison, and not just zero?
A quick refresher on error tolerances...
Suppose you have a value which should be exactly 0.5, but you calculate
it as 0.51. Then the absolute error is 0.51-0.5 = 0.01, and the relative
error is 0.01/0.5 = 0.02 (or 2%).
But consider two values, 0.0 and 0.1. Then the absolute error is 0.1-0.0
= 0.1, and the relative error is 0.1/0.0 which is infinite.
So 0.0 and -0.0 are problematic when dealing with relative errors.
> So isn't the issue about any n distance from any floating point number that
> is less than 1 ulp? And in that regard, comparison to zero is no different
> than any comparison to any other floating point value?
No. 1 ULP (Unit In Last Place) is the smallest possible difference
between two floats. A difference of 0 ULP means the two floats are
How it works: in mathematics, real numbers are continuous, but floats
are not. There are only 2**64 floats in Python (a C double), less if you
ignore the NANs and INFs, which means we can conveniently enumerate them
from -(2**64) to (2**64-1), based on the internal structure of a float.
So if you convert two floats into this enumerated integer value (which
is equivalent to doing a type-cast from a C double to a C long) and
subtract the two ints, this gives you a measure of how far apart they
are. (As Mark mentioned earlier, you have to make allowance for negative
floats, also INF and NANs are problematic too.)
If two values are exactly equal, their "distance apart" in ULP will be
zero. A distance of 1 ULP means they are consecutive floats, they cannot
possibly be any closer without being equal. A distance of 2 ULP means
there is only a single float separating them, and so on.
Note that ULP do not directly correspond to a numeric tolerance. For
example, these pairs of values are each 1 ULP apart:
0.0 and 5e-324
1.0 and 1.0000000000000002
1e300 and 1.0000000000000002e+300
So in these three cases, 1 ULP represents numeric differences of:
> Just trying to follow along,
A good resource is Bruce Dawson's blog RandomASCII, if you don't mind
the focus on C++. Start here:
More information about the Python-ideas