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What does sys.float_info.epsilon return?

On my system I get:

>>> sys.float_info.epsilon 2.220446049250313e-16 >>> sys.float_info.epsilon / 2 1.1102230246251565e-16 >>> 0 < sys.float_info.epsilon / 2 < sys.float_info.epsilon True

How is this possible?

EDIT:

You are all right, I thought epsilon does what min does. So I actually meant sys.float_info.min.

EDIT2

Everybody and especially John Kugelman, thanks for your answers!

Some playing around I did to clarify things to myself:

>>> float.hex(sys.float_info.epsilon) '0x1.0000000000000p-52' >>> float.hex(sys.float_info.min) '0x1.0000000000000p-1022' >>> float.hex(1 + a) '0x1.0000000000001p+0' >>> float.fromhex('0x0.0000000000001p+0') == sys.float_info.epsilon True >>> float.hex(sys.float_info.epsilon * sys.float_info.min) '0x0.0000000000001p-1022'

So epsilon * min gives the number with the smallest positive significand (or mantissa) and the smallest exponent.

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6 Answers 6

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5

epsilon is the difference between 1 and the next representable float. That's not the same as the smallest float, which would be the closest number to 0, not 1.

There are two smallest floats, depending on your criteria. min is the smallest normalized float. The smallest subnormal float is min * epsilon.

>>> sys.float_info.min 2.2250738585072014e-308 >>> sys.float_info.min * sys.float_info.epsilon 5e-324

Note the distinction between normalized and subnormal floats: min is not actually the smallest float, it's just the smallest one with full precision. Subnormal numbers cover the range between 0 and min, but they lose a lot of precision. Notice that 5e-324 has only one significant digit. Subnormals are also much slower to work with, up to 100x slower than normalized floats.

>>> (sys.float_info.min * sys.float_info.epsilon) / 2 0.0 >>> 4e-324 5e-324 >>> 5e-325 0.0

These tests confirm that 5e-324 truly is the smallest float. Dividing by two underflows to 0.

See also: What is the range of values a float can have in Python?

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2 Comments

My example works the same way for sys.float_info.min instead of sys.float_info.epsilon everywhere. How do you explain this?
Anything smaller than min is subnormal. min * epsilon is the true smallest float. If you divide it by 2 it underflows to 0.
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You actually want sys.float_info.min ("minimum positive normalized float"), which on machine gives me .2250738585072014e-308.

epsilon is:

difference between 1 and the least value greater than 1 that is representable as a float

See the docs for more info on the fields of sys.float_info.

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3 Comments

min still isn't quite the smallest positive float; there are still subnormals below it.
Thanks, you are right, but how do you explain what @user2357112 said?
Subnormals floats mean the number has zeros in its significand; I think @John Kugelman has the better answer at this point: I'd accept that one if it works for you, otherwise I can update this one if it doesn't.
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Your last expression is possible, because for any real, positive number, 0 < num/2 < num.

From the docs:

difference between 1 and the least value greater than 1 that is representable as a float

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sys.float_info is defined as

difference between 1 and the least value greater than 1 that is representable as a float

on this page.

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The documentation defines sys.float_info.epsilon as the

difference between 1 and the least value greater than 1 that is representable as a float

However, the gap between successive floats is bigger for bigger floats, so the gap between epsilon and the next smaller float is a lot smaller than epsilon. In particular, the next smaller float is not 0.

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Like every answer says, it's the difference between 1 and the next greatest value that can be represented, if you tried to add half of it to 1, you'll get 1 back

>>> (1 + (sys.float_info.epsilon/2)) == 1 True

Additionally if you try to add two thirds of it to 1, you'll get the same value:

>>> (1 + sys.float_info.epsilon) == (1 + (sys.float_info.epsilon * (2./3))) True
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