A manual that I am currently studying (I am a newbie) says:
"Numbers which differ by less than machine epsilon are numerically the same"
With Python, machine epsilon for float values can be obtained by typing
eps = numpy.finfo(float).eps
Now, If I check
1 + eps/10 != 1
I obtain False.
But If I check
0.1 + eps/10 != 0.1
I obtain True.
My latter logical expression turns to be False if I divide eps by 100. So, how does machine epsilon work? The Python documentation just says
"The smallest representable positive number such that 1.0 + eps != 1.0. Type of eps is an appropriate floating point type."
Thank you in advance.
Floating point numbers have a certain precision, to a few decimal places in scientific notation. The larger the number, the larger the least significant digit in that representation, and thus the larger the "epsilon" that could contribute to that number.
Thus, the epsilon is relative to the number it is added to, which is in fact stated in the documentation you cited: "... such that 1.0 + eps != 1.0". If the "reference" number is smaller by, e.g. one order of magnitude, then eps is smaller, too.
If that was not the case, you could not calculate at all with numbers smaller than eps (2.2e-16 in my case).