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Fixed Point Numbers

by ikegami (Pope)
on Nov 11, 2004 at 18:12 UTC ( #407136=note: print w/ replies, xml ) Need Help??


in reply to Re^2: Converting decimals to binary
in thread Converting decimals to binary

You can read the following more easily if you go follow this link

I don't know of any sources, but here's some background. Let's use smaller numbers to demonstrate. Let's say we have 4 bit numb +ers. +---+---+---+---+ | | | | | +---+---+---+---+ As a little refresher, let's forget decimal numbers for a second. +---+---+---+---+ | 0 | 1 | 1 | 1 | 7 = 0*2^3 + 1*2^2 + 1*2^1 + 0*2^0 +---+---+---+---+ I'm going to call that B4, since 4 bits are used by the integer portio +n of the number. +---+---+---+---+ | 0 | 1 | 1 | 1 | 7 B4 +---+---+---+---+ So how would you store a decimal number? Let's take 1.75, for example. +---+---+---+---+ + + | 0 | 0 | 0 | 1 | 1 1 1.75 = 1*2^0 + 1*2^(-1) + 1*2^(-2) +---+---+---+---+ + + Unfortunately, I can't just add bits to my register. What if we shifted the bits? +---+---+---+---+ | 0 | 1 | 1 | 1 | 1.75 * 2^2 = [ 1*2^0 + 1*2^(-1) + 1*2^(-2) ] * 2 +^2 +---+---+---+---+ Another way of phrasing that is in terms of number of integer bits lef +t. +---+---+---+---+ | 0 | 1 | 1 | 1 | 1.75 B2 = [ 1*2^0 + 1*2^(-1) + 1*2^(-2) ] * 2^(4 +-2) +---+---+---+---+ In other words, using smaller B-scalings increases precision: The following show the precision for a given scaling. +---+---+---+---+ | 0 | 0 | 0 | 1 | [ 1*2^0 ] * 2^(4-4) = 1 B4 +---+---+---+---+ +---+---+---+---+ | 0 | 0 | 0 | 1 | [ 1*2^(-1) ] * 2^(4-3) = 0.5 B3 +---+---+---+---+ +---+---+---+---+ | 0 | 0 | 0 | 1 | [ 1*2^(-2) ] * 2^(4-2) = 0.25 B2 +---+---+---+---+ +---+---+---+---+ | 0 | 0 | 0 | 1 | [ 1*2^(-3) ] * 2^(4-1) = 0.125 B1 +---+---+---+---+ +---+---+---+---+ | 0 | 0 | 0 | 1 | [ 1*2^(-4) ] * 2^(4-0) = 0.0625 B0 +---+---+---+---+ Nothing says we have to stop at 0. +---+---+---+---+ | 0 | 0 | 0 | 1 | [ 1*2^(-5) ] * 2^(4--1) = 0.03125 B-1 +---+---+---+---+ The downside is that biggest number we can represent goes down as the precision increases. +---+---+---+---+ | 1 | 1 | 1 | 1 | [ 1*2^3 + 1*2^2 + 1*2^1 + 1*2^0 ] * +2^(4-4) = 15 B4 +---+---+---+---+ +---+---+---+---+ | 1 | 1 | 1 | 1 | [ 1*2^2 + 1*2^1 + 1*2^0 + 1*2^(-1) ] * +2^(4-3) = 7.5 B3 +---+---+---+---+ +---+---+---+---+ | 1 | 1 | 1 | 1 | [ 1*2^1 + 1*2^0 + 1*2^(-1) + 1*2^(-2) ] * +2^(4-2) = 3.75 B2 +---+---+---+---+ +---+---+---+---+ | 1 | 1 | 1 | 1 | [ 1*2^0 + 1*2^(-1) + 1*2^(-2) + 1*2^(-3) ] * +2^(4-1) = 1.875 B1 +---+---+---+---+ +---+---+---+---+ | 1 | 1 | 1 | 1 | [ 1*2^(-1) + 1*2^(-2) + 1*2^(-3) + 1*2^(-4) ] * +2^(4-0) = 0.9375 B0 +---+---+---+---+ +---+---+---+---+ | 1 | 1 | 1 | 1 | [ 1*2^(-2) + 1*2^(-3) + 1*2^(-4) + 1*2^(-5) ] * +2^(4--1) = 0.46875 B-1 +---+---+---+---+ We can go the other way, if need be. +---+---+---+---+ | 1 | 1 | 1 | 1 | [ 1*2^4 + 1*2^3 + 1*2^2 + 1*2^1 ] * +2^(4-5) = 30 B5 +---+---+---+---+ +---+---+---+---+ | 0 | 0 | 0 | 1 | [ 1*2^1 ] * 2^(4-5) = 2 B5 +---+---+---+---+ <blockquote><i>How does multiplying by 4294967296 not include 1 but mu +ltiplying by 2147483648 does?</i></blockquote> Multiplying by 4294967296 (2^32) gives 32 bits for the decimal portion +, leaving no bits (32-32) for the integer portion. In other words, B0. 1 does not fit into no bits. + +---+----- 1 | 0 | ... 1 B0 + +---+----- Multiplying by 2147483648 (2^31) gives 31 bits for the decimal portion +, leaving 1 bit (32-31) for the integer portion. In other words, B1. 1 fits in one bit. +---+---+----- | 1 | 0 | ... 1 B1 +---+---+----- So what's the advantage over floating-point numbers? Floating-point numbers does this the same way, and it does it for you. However, in order to do that, it must save the scaling (called "expone +nt") along with the number. That means floats require more memory to store +than these. So what good is any of this anyway? You can use integer arithmetic on these numbers! X Bi + Y Bi = (X+Y) Bi X Bi - Y Bi = (X-Y) Bi X Bi * Y Bj = (X*Y) B(i+j) X Bi / Y Bj = (X/Y) B(i-j) X Bi >> j = X B(i+j) X Bi << j = X B(i-j) You can also compare numbers of the same scaling. X Bi < Y Bi X Bi > Y Bi X Bi == Y Bi etc. Do be careful about overflow! A = ... # B1 B = ... # B1 C = A + B # POTENTIAL OVERFLOW! # 1 B1 + 1 B1 will overflow, for example There are two ways of avoiding overflow. You can make sure in advance that the numbers won't cause an overflow, + or you can switch to a different B-scaling (at the cost of a bit of preci +sion). A = ... # B1 A = A >> 1 # B2 B = ... # B1 B = B >> 1 # B2 C = A + B # B2 We do lots of works with values in [0.0..1.0]. When we need to calcula +te by how much a valve should be open, we calculate it using values in [0.0. +.1.0]. Later on, we convert them to the right value to send to the Analog Out +put. It sounds like you want to deal with numbers in this same range, so I +thought the following would be very useful to you: A = ... # B1 Must be between 0.0 and 1.0. B = ... # B1 Must be between 0.0 and 1.0. C = A * B # B2 C = C << 1 # B1 Safe, since 1.0 * 1.0 = 1.0. Although we've only dealt unsigned numbers, everything I've said so far applies to signed 2s complement numbers.


Comment on Fixed Point Numbers
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Re: Fixed Point Numbers
by Roger (Parson) on Nov 12, 2004 at 15:22 UTC
    Nice work ikegami, I up voted you on this one. You *must* have a lot of free time up your sleeves. ;-)

      Thanks! But the only time it took was my lunch break. :) (ok, I probably overran my lunch a bit)

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