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Is there a standard value for (or method for obtaining) epsilon in Python? I need to compare floating point values and want to compare against the smallest possible difference.
In C++ there’s a function provided
numeric_limits::epsilon( ) which gives the epsilon value for any given data type. Is there an equivalent in Python?
The information is available in
sys.float_info, which corresponds to float.h in C99.
>>> import sys >>> sys.float_info.epsilon 2.220446049250313e-16
As strcat posted, there is
But don’t forget the pitfalls of using it as an absolute error margin for floating point comparisons. E.g. for large numbers, rounding error could exceed epsilon.
If you think you need a refresher, the standard reference is David Goldberg’s What Every Computer Scientist Should Know About Floating-Point Arithmetic, or for a simpler review you can check out The Floating Point Guide.
If you cannot find a function to do that, remember that the algorithm to calculate the machine epsilon is very easy (you can test with your favourite programming language).E.g, for python:
eps = 1.0 while eps + 1 > 1: eps /= 2 eps *= 2 print("The machine epsilon is:", eps)
In my case, I got:
The machine epsilon is: 2.220446049250313e-16
Surprised nobody mentioned this here; I think many people would use numpy.finfo( type(variable) ).eps instead. Or
.resolution if it is to assess precision.
finfo is only for floating point types, and that it also works with Python’s own
float type (i.e. not restricted to numpy’s types). The equivalent for integer types is
iinfo, though it does not contain precision information (because, well, why would it?).
The following worked for me as well:
>>> import math >>> math.ulp(1.0) 2.220446049250313e-16