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I am trying to understand floating point arithmetic better and have seen a few links to 'What Every Computer Scientist Should Know About Floating Point Arithmetic'.
I still don't understand how a number like 0.1 or 0.5 is stored in floats and as decimals.
Can someone please explain how it is laid out is memory?
I know about the float being two parts (i.e., a number to the power of something).
696
As 0.1 cannot be perfectly represented in binary, while double has 15 to 16 decimal digits of precision, and float has only 7 . So, they both are less than 0.1 , while the double is more close to 0.1 .
For example, the decimal number 0.1 doesn't have an exact binary representation, so it's dangerous to use the == operator to compare it to another floating-point number.
Python float classWhen you use the print() function, you'll see that the number 0.1 is represented as 0.1 exactly. Internally, Python can only represent 0.1 approximately. To see how Python represents the 0.1 internally, you can use the format() function.
I've always pointed people towards Harald Schmidt's online converter, along with the Wikipedia IEEE754-1985 article with its nice pictures.
For those two specific values, you get (for 0.1):
s eeeeeeee mmmmmmmmmmmmmmmmmmmmmmm 1/n
0 01111011 10011001100110011001101
| || || || || || +- 8388608
| || || || || |+--- 2097152
| || || || || +---- 1048576
| || || || |+------- 131072
| || || || +-------- 65536
| || || |+----------- 8192
| || || +------------ 4096
| || |+--------------- 512
| || +---------------- 256
| |+------------------- 32
| +-------------------- 16
+----------------------- 2
The sign is positive, that's pretty easy.
The exponent is 64+32+16+8+2+1 = 123 - 127 bias = -4, so the multiplier is 2-4 or 1/16.
The mantissa is chunky. It consists of 1 (the implicit base) plus (for all those bits with each being worth 1/(2n) as n starts at 1 and increases to the right), {1/2, 1/16, 1/32, 1/256, 1/512, 1/4096, 1/8192, 1/65536, 1/131072, 1/1048576, 1/2097152, 1/8388608}.
When you add all these up, you get 1.60000002384185791015625.
When you multiply that by the multiplier, you get 0.100000001490116119384765625, which is why they say you cannot represent 0.1 exactly as an IEEE754 float, and provides so much opportunity on SO for people answering "why doesn't 0.1 + 0.1 + 0.1 == 0.3?"-type questions :-)
The 0.5 example is substantially easier. It's represented as:
s eeeeeeee mmmmmmmmmmmmmmmmmmmmmmm
0 01111110 00000000000000000000000
which means it's the implicit base, 1, plus no other additives (all the mantissa bits are zero).
The sign is again positive. The exponent is 64+32+16+8+4+2 = 126 - 127 bias = -1. Hence the multiplier is 2-1 which is 1/2 or 0.5.
So the final value is 1 multiplied by 0.5, or 0.5. Voila!
I've sometimes found it easier to think of it in terms of decimal.
The number 1.345 is equivalent to
1 + 3/10 + 4/100 + 5/1000
or:
-1 -2 -3
1 + 3*10 + 4*10 + 5*10
Similarly, the IEEE754 representation for decimal 0.8125 is:
s eeeeeeee mmmmmmmmmmmmmmmmmmmmmmm
0 01111110 10100000000000000000000
With the implicit base of 1, that's equivalent to the binary:
01111110-01111111
1.101 * 2
or:
-1
(1 + 1/2 + 1/8) * 2 (no 1/4 since that bit is 0)
which becomes:
(8/8 + 4/8 + 1/8) * 1/2
and then becomes:
13/8 * 1/2 = 0.8125
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