Where is the 6th bit in 16-bit char value? - java

There are two examples of bit switching in the book Java: A Beginners Guide. In both cases, the author writes about switching the 6th bit but he demonstrates it on 16 digits. Both examples use bitwise operators for changing the letter case.
In the first, he operates on lower case characters: 'a' & 65503 changes the char to 'A'. It is described as switching the 6th bit off. The problem is the number 65503 equals 1111 1111 1101 1111 in binary. Hence the 11th digit/bit is switched off (he even shows the number there).
The same he does for uppercase letters and bitwise OR operator: 'a' | 32 does the trick. The number 32 equals 0000 0000 0010 0000 in binary.
In both cases, the change does make sense. I just don’t understand why the author writes about the 6th bit. I would understand if it was for the 11th bit or 6th pair (in that case I would expect the turning it off completely as 00 or 11.
Any clarification more than welcome.

The location of the 6th bit could be in a few locations, depending upon the convention you adopt:
Counting the left-most bit as zero-th, and moving right
Counting the left-most bit as first, and moving right
Counting the right-most bit as zero-th, and moving left
Counting the right-most bit as first, and moving left
(Other conventions may be available)
The author should really have defined which of these he was using, for clarity (and may do, elsewhere in the text). But apparently, he means item 4.
0000 0000 0010 0000
65 4321

The 0's and 1's that make up a binary number are called bits. The bits start from the right and go left:
So 0010 0000:
8th bit 7th bit 6th bit 5th bit 4th bit 3rd bit 2nd bit 1st bit
0 0 1 0 0 0 0 0
Decimal is read the same way as binary:
3754 in decimal:
(3 x 1000) | (7 x 100) | (5 x 10) | (4 x 1)
156 in binary = 10011100
(1 x 128)|(0 x 64)|(0 x 32)|(1 x 16)|(1 x 8)|(0 x 4)|(0 x 2)|(0 x 1)
In decimal you add a new column to to the start of the number (i.e the right) when you reach a power of 10.
In binary you add a new column when you reach a power of 2.
Does this help explain it?

Related

Equivalent function for Math.signum(double) for int or long in Java

Is there an equivalent function for Math.signum(double) or Math.signum(float) in Java for other primitive numbers like int or long. I don't want to write code like
int sign = (int) Math.signum((double) intValue);
when there is a better alternative.
You can use it this way:
Integer.signum(int i) and Long.signum(long l)
Link to javadoc: https://docs.oracle.com/javase/8/docs/api/java/lang/Integer.html#signum-int-
Just an addendum of some implementation details of that :
public static int signum(int i) {
// HD, Section 2-7
return (i >> 31) | (-i >>> 31);
}
Integer::signum says : I'll give you -1 if the number is negative, 0 if number is zero and 1 if number is positive. This is fairly trivial via some nested if/else for example.
Instead JDK uses a solution that is a bit more fancy. (x >> 31) | (-x >>> 31). Looks easy right? the first part : x >> 31 is signed shift to the right; it's called signed because it keeps the sign after the shift.
Suppose we live in a world where 4 bits numbers exist only (for simplicity).
We have 0100 (+4), a single shift 0100 >> 1 will make it 0010 (+2). Now, if our number is 1100 (-4; the first bit is the sign), a shift signed to the right : 1100 >> 1 is 1110 (-2). Do a division, but keep the sign.
Thus if we shift 31 times, we throw away the last 31 bits of the number, move the bit for the sign in the least significant position and keep the original sign. Or in simple words take the 31 bit, put it into 0 position and throw away everything else.
0 00000 ..... 11111
x --------------->0 // x is kept
ignore
The second part -x >>> 31 is a unsigned shift, meaning the sign is not kept when we shift.
For example 0100 >>> 1 (+4) will give you 0010 (+2). Nothing is really different so far from the signed shift and the example above. The interesting part comes when numbers are negative:
1100 (-4) and we try to shift it once : 1100 >>> 1, because the sign is not kept, we put zero in the most significant bit and move to the right, thus we get : 0110 (+6!).
In reality, taking 32 bits into the picture. -4 == 1111...111100 and we shift it to the right: sign is zero, everything else is moved to the right, thus: 0111...11110 or Integer.MAX_VALUE - 1.
System.out.println(-4 >>> 1);
System.out.println(Integer.MAX_VALUE - 1);
Thus the part x >>> 31 will move the sign bit into the least significant position, zeroing everything else. No matter the number you give it, you will always get 1 or 0.
1 00000 ..... 11111
x --------------->1 // x is "zeroed also"
ignore
And the addition of -x to that x >>> 31 is simply done so that | would work correctly satisfy our needed result.

If given two hexadecimal numbers find if they can be consecutive in gray code

What is "consecutive in gray code" supposed to mean? I mean 10 and 11 are consecutive in decimal system but what is "consecutive in gray code" meaning? I only know gray code is a binary numeral system where two successive values differ in only one bit.
Here is a solution online but I cannot understand this
private static int graycode(byte term1, byte term2) {
byte x = (byte)(term1^term2); // why use XOR?
int count = 0;
while(x!=0)
{
x = (byte)(x &(x-1)); // why use bitwise operator?
count++; // what is count?
}
return count == 1;
}
I try to understand spending a hour but I still do not have a clue.
Two numbers are considered consecutive in gray code if they differ by only one bit in their binary representation e.g. 111 and 101 differ by only the 2nd bit. The function you have checks if two input bytes have only one bit that makes them different. So 111 and 101 would return 1 from the function whereas 111 and 100 would return 0.
XOR is used to find differences between both numbers; XOR yields 1 when bits are different and 0 otherwise e.g. 1111 XOR 1011 would give 0100. So with XOR, each bit difference is highlighted by a 1 in that position. If both numbers are consecutive gray codes then there should be only one 1 in the XOR's result. More than one 1 would indicate multiple differences thus failing the criterion. The XOR result is stored in variable x.
The next task is then to count the number of 1's -- hence the variable count. If you try other gray code pairs (of greater bit length), you will notice the XOR value obtained will always be in this format (neglecting leading zeros): 10, 100, 1000, etc. Basically, 1 followed by zeros or, in other words, always a power of 2.
If these sample XOR results were decremented by 1, you would get: 01, 011, 0111, etc. If these new values were ANDed with the original XOR results, 0 would be the result everytime. This is the logic implemented in your solution: for a consecutive gray code pair, the while loop would run only once (and increment count) after which it would terminate because x had become 0. So count = 1 at the end. For a non-consecutive pair, the loop would run more than once (try it) and count would be greater than 1 at the end.
The function uses this as a basis to return 1 if count == 1 and 0 otherwise.
A bit obscure but it gets the job done.
It means the two numbers differ in exactly one bit.
So the solution begins with xor'ing the two numbers. The xor operation results in a 1 where the bits of the operands differ, else zero.
So you need to count the number of bits in the xor result and compare with 1. That's what your downloaded example does. This method of counting 1's in a binary number is a rather well-known method due to Brian Kernighan. The state x = (byte)(x & (x-1)) is bit magic that resets the highest order 1 bit to zero. There are lots of others.
Alternately you could search a table of the 8 possible bytes with 1 bit.
byte one_bit_bytes[] = { 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80 };
It is a very non-intuitive way to count how many bits in a binary number are equal to '1'.
It requires a knowledge of binary arithmetic. Start with what happens when you subtract 1 for a decimal number which is written by a '1' followed by one or more zeroes: you get a sequence of 9's, which length is equal to the number of zeroes:
1000000 - 1 = 999999
A similar thing happens with binary numbers. If you subtract 1 from a non-negative binary number, all the lowest '0' digits are replaced by '1', and the '1' just before theses zeroes is replaced by zero. This follows from the way borrowing is done in binary. Example:
0101_0000_0001_0000 - 1 = 0101_0000_0000_1111
aaaa aaaa aaab cccc -> aaaa aaaa aaab cccc
Notation: Underscores to improve legibility. All the digits that appear above the letter a are unchanged. The digit '1' that appears above the letter b is changed to a '0'. And the digits '0' that appear above the letter c are changed to '1'.
The next step consists of doing a bitwise AND operation with the two numbers (X) and (X-1). With the arithmetic property described above, at each iteration there is exactly one '1' digit that disappear from the number (starting from the right, i.e. the least significant bit).
By counting the number of iterations, we can know how many '1' bits were initially present in number X. The iteration stops when the variable X equals zero.
Other people have already answered the question about gray code. My answer only explains how the "bit counting" works (after XOR'ing the two values).
Here is a naive test for a particular Gray code monotonic ordering (the binary reflected Gray code):
// convert Gray code binary number to base 2 binary number
int Base2(byte Gray){ Gray^=Gray>>4; Gray^=Gray>>2; return Gray^=Gray>>1; }
// test if Gray codes are consecutive using "normal" base 2 numbers
boolean GraysAdjacent(byte x, byte y){ return 1 == abs(Base2(x)-Base2(y)); }
see especially this answer (best):
How to find if two numbers are consecutive numbers in gray code sequence
coded in C as:
int GraysTouch(byte x, byte y){ return !( (x^y ^ 1) && ( x^y ^ (y&-y)<<1 ) ); }
// test x marks the spots! (where they touch!)
for(int i=31; i>-1; --i )
for(int j=31; j>-1; --j )
Serial.print((String)(GraysTouch( i^i>>1, j^j>>1 )?"x":".") +
(GraysTouch( j^j>>1, i^i>>1 )?"X":".") + (j?"":"\n"));
How this works: ... will be explained and not the OP code because it is highly suspect (see Caveats commentary below).
A property of XOR, aka the ^ operator, is that bits that match are 0 and bits that are different are 1.
1^0 == 0^1 == 1
1^1 == 0^0 == 0
Also, for a bit, 0 XOR b works as the identity function or simply b
and
1 XOR b works as the complement (no compliments please) function or ~b.
id(x) == x == x^0
opposite(x) == ~x == x^11111111 Why eight 1's? Are eight enough?
When comparing two bit strings with XOR, bits that are different XOR as 1, otherwise the bits must match and the XOR is 0 :
0101 0001111001100111000
XOR 0011 XOR 0001111001100000111
------ ---------------------
0110 0000000000000111111
This explains the x^y part of the code above.
----------------------------------------------------------------------
An aside:
n^n>>1 does a quick conversion from base 2 binary to the Gray code binary numbers used here.
Also note how potent it is that f(a,b)=a^b^b=a is idempotent for any b!
An in place swap is then a=a^b; b=a^b; a=a^b;.
Unrolled c=a^b; d=c^b; e=c^d; ie. d=a^b^b=a; e=a^b^a=b;
----------------------------------------------------------------------
Now, by definition, for two Gray coded numbers to be adjacent or consecutive there must be one and only one bit that can change and be different.
Examples:
Johnson
Code
000 000 000 000
001 001 001 100
011 101 011 110
111 111 010 010
110 011 110 011
100 010 111 111
110 101 101
100 100 001
^^^
this Gray coding
is the one used here
Examine it carefully.
Case 1
When the lowest order bit of consecutive numbers, x and y, for any of the Gray codes, are different, the rest must be the same! This is the definition of a Gray code. This means x^y must look like 0000...0001.
Remember complement, the ~ function aka 1^b? To test the last bit x^y is XOR'd with 1.
This explains the x^y ^ 1.
-------------------------------------------
Case 2
The location of the different bit in the consecutive Gray code numbers x and y is not the lowest order bit. Look carefully at these Gray code consecutive numbers.
001 010 101 lower order bits all match
011 110 111
| | | <-- | mark location of lowest 1
010 100 010 <-- XOR's
Interestingly, in this Gray code, when the lowest order bits match in x and y, so too does the location of the lowest order 1.
Even more interesting is that, for consecutive numbers, the bits are always different (for this Gray code) in the next higher order bit position!
So, x^y looks like ???...?1000...0 where 1000...0 must have at least one 0, 10 (Why?) and ???...? are the mystery bits that for consecutive Gray code numbers must be 000...0. (Why? ie. to be consecutive x^y must look like ... )
The observation is that
x^y looks like ???...?100...0 if and only if
x and y look like ???...?:10...0
| <-- remember? the 1 location !!
The | location can be found by either x&-x or y&-y. (Why? Why must the - be done using a 2's complement machine?)
However, the : location must be checked to see that it is 1 (Why?) and the ???...? are 000...0. (Why?)
So,
x^y looks like ???...?100...0 and
(y&-y)<<1 looks like 000...0100...0
and this explains the x^y ^ ((y&-y)<<1) test.
-------------------------------------------------------------------
Why this works: ... is a consequence of the properties of the particular Gray code used here. An examination and explanation is too complicated to be given here as to why this Gray code should have these properties.
----------------------------------------------------------------------
Commentary on the inadequacies of previous answers due to OP code issues.
Caveat 1: Just to be explicit, the algorithm in the OP's question:
private static int graycode(byte term1, byte term2) {
byte x = (byte)(term1^term2); // why use XOR?
int count = 0;
while(x!=0)
{
x = (byte)(x &(x-1)); // why use bitwise operator?
count++; // what is count?
}
return count == 1;
}
has an interesting interpretation of consecutive Gray codes. It does report correctly when any two binary sequences differ in a single bit position.
If, by consecutive codes it is meant that the Gray codes are used to enumerate a monotonic ordering, there is a problem.
Specifically, the code will return true for all these pairs:
000, 001 or 000, 010 or 000, 100
so an ordering might be 001, 000, 010 but then where can 100 go?
The algorithm reports (correctly) that the "consecutiveness" of 100 with either of 001 or 010 is false.
Thus 100 must immediately precede or follow 000 in an enumeration but cannot immediately precede or follow 001 or 010. DOH!!!
Caveat 2: Note x = (byte)(x & (x-1)) resets the lowest order 1 bit of x to zero.
refs:
Gray code increment function
Deriving nth Gray code from the (n-1)th Gray Code
https://electronics.stackexchange.com/questions/26677/3bit-gray-counter-using-d-flip-flops-and-logic-gates
How do I find next bit to change in a Gray code in constant time?
How to find if two numbers are consecutive numbers in gray code sequence

How to write BYTEA in postgresql jdbc prepared statements [duplicate]

I'm in a computer systems course and have been struggling, in part, with two's complement. I want to understand it, but everything I've read hasn't brought the picture together for me. I've read the Wikipedia article and various other articles, including my text book.
What is two's complement, how can we use it and how can it affect numbers during operations like casts (from signed to unsigned and vice versa), bit-wise operations and bit-shift operations?
Two's complement is a clever way of storing integers so that common math problems are very simple to implement.
To understand, you have to think of the numbers in binary.
It basically says,
for zero, use all 0's.
for positive integers, start counting up, with a maximum of 2(number of bits - 1)-1.
for negative integers, do exactly the same thing, but switch the role of 0's and 1's and count down (so instead of starting with 0000, start with 1111 - that's the "complement" part).
Let's try it with a mini-byte of 4 bits (we'll call it a nibble - 1/2 a byte).
0000 - zero
0001 - one
0010 - two
0011 - three
0100 to 0111 - four to seven
That's as far as we can go in positives. 23-1 = 7.
For negatives:
1111 - negative one
1110 - negative two
1101 - negative three
1100 to 1000 - negative four to negative eight
Note that you get one extra value for negatives (1000 = -8) that you don't for positives. This is because 0000 is used for zero. This can be considered as Number Line of computers.
Distinguishing between positive and negative numbers
Doing this, the first bit gets the role of the "sign" bit, as it can be used to distinguish between nonnegative and negative decimal values. If the most significant bit is 1, then the binary can be said to be negative, where as if the most significant bit (the leftmost) is 0, you can say the decimal value is nonnegative.
"Sign-magnitude" negative numbers just have the sign bit flipped of their positive counterparts, but this approach has to deal with interpreting 1000 (one 1 followed by all 0s) as "negative zero" which is confusing.
"Ones' complement" negative numbers are just the bit-complement of their positive counterparts, which also leads to a confusing "negative zero" with 1111 (all ones).
You will likely not have to deal with Ones' Complement or Sign-Magnitude integer representations unless you are working very close to the hardware.
I wonder if it could be explained any better than the Wikipedia article.
The basic problem that you are trying to solve with two's complement representation is the problem of storing negative integers.
First, consider an unsigned integer stored in 4 bits. You can have the following
0000 = 0
0001 = 1
0010 = 2
...
1111 = 15
These are unsigned because there is no indication of whether they are negative or positive.
Sign Magnitude and Excess Notation
To store negative numbers you can try a number of things. First, you can use sign magnitude notation which assigns the first bit as a sign bit to represent +/- and the remaining bits to represent the magnitude. So using 4 bits again and assuming that 1 means - and 0 means + then you have
0000 = +0
0001 = +1
0010 = +2
...
1000 = -0
1001 = -1
1111 = -7
So, you see the problem there? We have positive and negative 0. The bigger problem is adding and subtracting binary numbers. The circuits to add and subtract using sign magnitude will be very complex.
What is
0010
1001 +
----
?
Another system is excess notation. You can store negative numbers, you get rid of the two zeros problem but addition and subtraction remains difficult.
So along comes two's complement. Now you can store positive and negative integers and perform arithmetic with relative ease. There are a number of methods to convert a number into two's complement. Here's one.
Convert Decimal to Two's Complement
Convert the number to binary (ignore the sign for now)
e.g. 5 is 0101 and -5 is 0101
If the number is a positive number then you are done.
e.g. 5 is 0101 in binary using two's complement notation.
If the number is negative then
3.1 find the complement (invert 0's and 1's)
e.g. -5 is 0101 so finding the complement is 1010
3.2 Add 1 to the complement 1010 + 1 = 1011.
Therefore, -5 in two's complement is 1011.
So, what if you wanted to do 2 + (-3) in binary? 2 + (-3) is -1.
What would you have to do if you were using sign magnitude to add these numbers? 0010 + 1101 = ?
Using two's complement consider how easy it would be.
2 = 0010
-3 = 1101 +
-------------
-1 = 1111
Converting Two's Complement to Decimal
Converting 1111 to decimal:
The number starts with 1, so it's negative, so we find the complement of 1111, which is 0000.
Add 1 to 0000, and we obtain 0001.
Convert 0001 to decimal, which is 1.
Apply the sign = -1.
Tada!
Like most explanations I've seen, the ones above are clear about how to work with 2's complement, but don't really explain what they are mathematically. I'll try to do that, for integers at least, and I'll cover some background that's probably familiar first.
Recall how it works for decimal: 2345 is a way of writing 2 × 103 + 3 × 102 + 4 × 101 + 5 × 100.
In the same way, binary is a way of writing numbers using just 0 and 1 following the same general idea, but replacing those 10s above with 2s. Then in binary, 1111is a way of writing 1 × 23 + 1 × 22 + 1 × 21 + 1 × 20and if you work it out, that turns out to equal 15 (base 10). That's because it is 8+4+2+1 = 15.
This is all well and good for positive numbers. It even works for negative numbers if you're willing to just stick a minus sign in front of them, as humans do with decimal numbers. That can even be done in computers, sort of, but I haven't seen such a computer since the early 1970's. I'll leave the reasons for a different discussion.
For computers it turns out to be more efficient to use a complement representation for negative numbers. And here's something that is often overlooked. Complement notations involve some kind of reversal of the digits of the number, even the implied zeroes that come before a normal positive number. That's awkward, because the question arises: all of them? That could be an infinite number of digits to be considered.
Fortunately, computers don't represent infinities. Numbers are constrained to a particular length (or width, if you prefer). So let's return to positive binary numbers, but with a particular size. I'll use 8 digits ("bits") for these examples. So our binary number would really be 00001111or 0 × 27 + 0 × 26 + 0 × 25 + 0 × 24 + 1 × 23 + 1 × 22 + 1 × 21 + 1 × 20
To form the 2's complement negative, we first complement all the (binary) digits to form 11110000and add 1 to form 11110001but how are we to understand that to mean -15?
The answer is that we change the meaning of the high-order bit (the leftmost one). This bit will be a 1 for all negative numbers. The change will be to change the sign of its contribution to the value of the number it appears in. So now our 11110001 is understood to represent -1 × 27 + 1 × 26 + 1 × 25 + 1 × 24 + 0 × 23 + 0 × 22 + 0 × 21 + 1 × 20Notice that "-" in front of that expression? It means that the sign bit carries the weight -27, that is -128 (base 10). All the other positions retain the same weight they had in unsigned binary numbers.
Working out our -15, it is -128 + 64 + 32 + 16 + 1 Try it on your calculator. it's -15.
Of the three main ways that I've seen negative numbers represented in computers, 2's complement wins hands down for convenience in general use. It has an oddity, though. Since it's binary, there have to be an even number of possible bit combinations. Each positive number can be paired with its negative, but there's only one zero. Negating a zero gets you zero. So there's one more combination, the number with 1 in the sign bit and 0 everywhere else. The corresponding positive number would not fit in the number of bits being used.
What's even more odd about this number is that if you try to form its positive by complementing and adding one, you get the same negative number back. It seems natural that zero would do this, but this is unexpected and not at all the behavior we're used to because computers aside, we generally think of an unlimited supply of digits, not this fixed-length arithmetic.
This is like the tip of an iceberg of oddities. There's more lying in wait below the surface, but that's enough for this discussion. You could probably find more if you research "overflow" for fixed-point arithmetic. If you really want to get into it, you might also research "modular arithmetic".
2's complement is very useful for finding the value of a binary, however I thought of a much more concise way of solving such a problem(never seen anyone else publish it):
take a binary, for example: 1101 which is [assuming that space "1" is the sign] equal to -3.
using 2's complement we would do this...flip 1101 to 0010...add 0001 + 0010 ===> gives us 0011. 0011 in positive binary = 3. therefore 1101 = -3!
What I realized:
instead of all the flipping and adding, you can just do the basic method for solving for a positive binary(lets say 0101) is (23 * 0) + (22 * 1) + (21 * 0) + (20 * 1) = 5.
Do exactly the same concept with a negative!(with a small twist)
take 1101, for example:
for the first number instead of 23 * 1 = 8 , do -(23 * 1) = -8.
then continue as usual, doing -8 + (22 * 1) + (21 * 0) + (20 * 1) = -3
Imagine that you have a finite number of bits/trits/digits/whatever. You define 0 as all digits being 0, and count upwards naturally:
00
01
02
..
Eventually you will overflow.
98
99
00
We have two digits and can represent all numbers from 0 to 100. All those numbers are positive! Suppose we want to represent negative numbers too?
What we really have is a cycle. The number before 2 is 1. The number before 1 is 0. The number before 0 is... 99.
So, for simplicity, let's say that any number over 50 is negative. "0" through "49" represent 0 through 49. "99" is -1, "98" is -2, ... "50" is -50.
This representation is ten's complement. Computers typically use two's complement, which is the same except using bits instead of digits.
The nice thing about ten's complement is that addition just works. You do not need to do anything special to add positive and negative numbers!
I read a fantastic explanation on Reddit by jng, using the odometer as an analogy.
It is a useful convention. The same circuits and logic operations that
add / subtract positive numbers in binary still work on both positive
and negative numbers if using the convention, that's why it's so
useful and omnipresent.
Imagine the odometer of a car, it rolls around at (say) 99999. If you
increment 00000 you get 00001. If you decrement 00000, you get 99999
(due to the roll-around). If you add one back to 99999 it goes back to
00000. So it's useful to decide that 99999 represents -1. Likewise, it is very useful to decide that 99998 represents -2, and so on. You have
to stop somewhere, and also by convention, the top half of the numbers
are deemed to be negative (50000-99999), and the bottom half positive
just stand for themselves (00000-49999). As a result, the top digit
being 5-9 means the represented number is negative, and it being 0-4
means the represented is positive - exactly the same as the top bit
representing sign in a two's complement binary number.
Understanding this was hard for me too. Once I got it and went back to
re-read the books articles and explanations (there was no internet
back then), it turned out a lot of those describing it didn't really
understand it. I did write a book teaching assembly language after
that (which did sell quite well for 10 years).
Two complement is found out by adding one to 1'st complement of the given number.
Lets say we have to find out twos complement of 10101 then find its ones complement, that is, 01010 add 1 to this result, that is, 01010+1=01011, which is the final answer.
Lets get the answer 10 – 12 in binary form using 8 bits:
What we will really do is 10 + (-12)
We need to get the compliment part of 12 to subtract it from 10.
12 in binary is 00001100.
10 in binary is 00001010.
To get the compliment part of 12 we just reverse all the bits then add 1.
12 in binary reversed is 11110011. This is also the Inverse code (one's complement).
Now we need to add one, which is now 11110100.
So 11110100 is the compliment of 12! Easy when you think of it this way.
Now you can solve the above question of 10 - 12 in binary form.
00001010
11110100
-----------------
11111110
Looking at the two's complement system from a math point of view it really makes sense. In ten's complement, the idea is to essentially 'isolate' the difference.
Example: 63 - 24 = x
We add the complement of 24 which is really just (100 - 24). So really, all we are doing is adding 100 on both sides of the equation.
Now the equation is: 100 + 63 - 24 = x + 100, that is why we remove the 100 (or 10 or 1000 or whatever).
Due to the inconvenient situation of having to subtract one number from a long chain of zeroes, we use a 'diminished radix complement' system, in the decimal system, nine's complement.
When we are presented with a number subtracted from a big chain of nines, we just need to reverse the numbers.
Example: 99999 - 03275 = 96724
That is the reason, after nine's complement, we add 1. As you probably know from childhood math, 9 becomes 10 by 'stealing' 1. So basically it's just ten's complement that takes 1 from the difference.
In Binary, two's complement is equatable to ten's complement, while one's complement to nine's complement. The primary difference is that instead of trying to isolate the difference with powers of ten (adding 10, 100, etc. into the equation) we are trying to isolate the difference with powers of two.
It is for this reason that we invert the bits. Just like how our minuend is a chain of nines in decimal, our minuend is a chain of ones in binary.
Example: 111111 - 101001 = 010110
Because chains of ones are 1 below a nice power of two, they 'steal' 1 from the difference like nine's do in decimal.
When we are using negative binary number's, we are really just saying:
0000 - 0101 = x
1111 - 0101 = 1010
1111 + 0000 - 0101 = x + 1111
In order to 'isolate' x, we need to add 1 because 1111 is one away from 10000 and we remove the leading 1 because we just added it to the original difference.
1111 + 1 + 0000 - 0101 = x + 1111 + 1
10000 + 0000 - 0101 = x + 10000
Just remove 10000 from both sides to get x, it's basic algebra.
The word complement derives from completeness. In the decimal world the numerals 0 through 9 provide a complement (complete set) of numerals or numeric symbols to express all decimal numbers. In the binary world the numerals 0 and 1 provide a complement of numerals to express all binary numbers. In fact The symbols 0 and 1 must be used to represent everything (text, images, etc) as well as positive (0) and negative (1).
In our world the blank space to the left of number is considered as zero:
35=035=000000035.
In a computer storage location there is no blank space. All bits (binary digits) must be either 0 or 1. To efficiently use memory numbers may be stored as 8 bit, 16 bit, 32 bit, 64 bit, 128 bit representations. When a number that is stored as an 8 bit number is transferred to a 16 bit location the sign and magnitude (absolute value) must remain the same. Both 1's complement and 2's complement representations facilitate this.
As a noun:
Both 1's complement and 2's complement are binary representations of signed quantities where the most significant bit (the one on the left) is the sign bit. 0 is for positive and 1 is for negative.
2s complement does not mean negative. It means a signed quantity. As in decimal the magnitude is represented as the positive quantity. The structure uses sign extension to preserve the quantity when promoting to a register [] with more bits:
[0101]=[00101]=[00000000000101]=5 (base 10)
[1011]=[11011]=[11111111111011]=-5(base 10)
As a verb:
2's complement means to negate. It does not mean make negative. It means if negative make positive; if positive make negative. The magnitude is the absolute value:
if a >= 0 then |a| = a
if a < 0 then |a| = -a = 2scomplement of a
This ability allows efficient binary subtraction using negate then add.
a - b = a + (-b)
The official way to take the 1's complement is for each digit subtract its value from 1.
1'scomp(0101) = 1010.
This is the same as flipping or inverting each bit individually. This results in a negative zero which is not well loved so adding one to te 1's complement gets rid of the problem.
To negate or take the 2s complement first take the 1s complement then add 1.
Example 1 Example 2
0101 --original number 1101
1's comp 1010 0010
add 1 0001 0001
2's comp 1011 --negated number 0011
In the examples the negation works as well with sign extended numbers.
Adding:
1110 Carry 111110 Carry
0110 is the same as 000110
1111 111111
sum 0101 sum 000101
SUbtracting:
1110 Carry 00000 Carry
0110 is the same as 00110
-0111 +11001
---------- ----------
sum 0101 sum 11111
Notice that when working with 2's complement, blank space to the left of the number is filled with zeros for positive numbers butis filled with ones for negative numbers. The carry is always added and must be either a 1 or 0.
Cheers
2's complement is essentially a way of coming up with the additive inverse of a binary number. Ask yourself this: Given a number in binary form (present at a fixed length memory location), what bit pattern, when added to the original number (at the fixed length memory location), would make the result all zeros ? (at the same fixed length memory location). If we could come up with this bit pattern then that bit pattern would be the -ve representation (additive inverse) of the original number; as by definition adding a number to its additive inverse always results in zero. Example: take 5 which is 101 present inside a single 8 bit byte. Now the task is to come up with a bit pattern which when added to the given bit pattern (00000101) would result in all zeros at the memory location which is used to hold this 5 i.e. all 8 bits of the byte should be zero. To do that, start from the right most bit of 101 and for each individual bit, again ask the same question: What bit should I add to the current bit to make the result zero ? continue doing that taking in account the usual carry over. After we are done with the 3 right most places (the digits that define the original number without regard to the leading zeros) the last carry goes in the bit pattern of the additive inverse. Furthermore, since we are holding in the original number in a single 8 bit byte, all other leading bits in the additive inverse should also be 1's so that (and this is important) when the computer adds "the number" (represented using the 8 bit pattern) and its additive inverse using "that" storage type (a byte) the result in that byte would be all zeros.
1 1 1
----------
1 0 1
1 0 1 1 ---> additive inverse
---------
0 0 0
Many of the answers so far nicely explain why two's complement is used to represent negative numbers, but do not tell us what two's complement number is, particularly not why a '1' is added, and in fact often added in a wrong way.
The confusion comes from a poor understanding of the definition of a complement number. A complement is the missing part that would make something complete.
The radix complement of an n digit number x in radix b is, by definition, b^n-x.
In binary 4 is represented by 100, which has 3 digits (n=3) and a radix of 2 (b=2). So its radix complement is b^n-x = 2^3-4=8-4=4 (or 100 in binary).
However, in binary obtaining a radix's complement is not as easy as getting its diminished radix complement, which is defined as (b^n-1)-y, just 1 less than that of radix complement. To get a diminished radix complement, you simply flip all the digits.
100 -> 011 (diminished (one's) radix complement)
to obtain the radix (two's) complement, we simply add 1, as the definition defined.
011 +1 ->100 (two's complement).
Now with this new understanding, let's take a look of the example given by Vincent Ramdhanie (see above second response):
Converting 1111 to decimal:
The number starts with 1, so it's negative, so we find the complement of 1111, which is 0000.
Add 1 to 0000, and we obtain 0001.
Convert 0001 to decimal, which is 1.
Apply the sign = -1.
Tada!
Should be understood as:
The number starts with 1, so it's negative. So we know it is a two's complement of some value x. To find the x represented by its two's complement, we first need find its 1's complement.
two's complement of x: 1111
one's complement of x: 1111-1 ->1110;
x = 0001, (flip all digits)
Apply the sign -, and the answer =-x =-1.
I liked lavinio's answer, but shifting bits adds some complexity. Often there's a choice of moving bits while respecting the sign bit or while not respecting the sign bit. This is the choice between treating the numbers as signed (-8 to 7 for a nibble, -128 to 127 for bytes) or full-range unsigned numbers (0 to 15 for nibbles, 0 to 255 for bytes).
It is a clever means of encoding negative integers in such a way that approximately half of the combination of bits of a data type are reserved for negative integers, and the addition of most of the negative integers with their corresponding positive integers results in a carry overflow that leaves the result to be binary zero.
So, in 2's complement if one is 0x0001 then -1 is 0x1111, because that will result in a combined sum of 0x0000 (with an overflow of 1).
2’s Complements: When we add an extra one with the 1’s complements of a number we will get the 2’s complements. For example: 100101 it’s 1’s complement is 011010 and 2’s complement is 011010+1 = 011011 (By adding one with 1's complement) For more information
this article explain it graphically.
Two's complement is mainly used for the following reasons:
To avoid multiple representations of 0
To avoid keeping track of carry bit (as in one's complement) in case of an overflow.
Carrying out simple operations like addition and subtraction becomes easy.
Two's complement is one of the ways of expressing a negative number and most of the controllers and processors store a negative number in two's complement form.
In simple terms, two's complement is a way to store negative numbers in computer memory. Whereas positive numbers are stored as a normal binary number.
Let's consider this example,
The computer uses the binary number system to represent any number.
x = 5;
This is represented as 0101.
x = -5;
When the computer encounters the - sign, it computes its two's complement and stores it.
That is, 5 = 0101 and its two's complement is 1011.
The important rules the computer uses to process numbers are,
If the first bit is 1 then it must be a negative number.
If all the bits except first bit are 0 then it is a positive number, because there is no -0 in number system (1000 is not -0 instead it is positive 8).
If all the bits are 0 then it is 0.
Else it is a positive number.
To bitwise complement a number is to flip all the bits in it. To two’s complement it, we flip all the bits and add one.
Using 2’s complement representation for signed integers, we apply the 2’s complement operation to convert a positive number to its negative equivalent and vice versa. So using nibbles for an example, 0001 (1) becomes 1111 (-1) and applying the op again, returns to 0001.
The behaviour of the operation at zero is advantageous in giving a single representation for zero without special handling of positive and negative zeroes. 0000 complements to 1111, which when 1 is added. overflows to 0000, giving us one zero, rather than a positive and a negative one.
A key advantage of this representation is that the standard addition circuits for unsigned integers produce correct results when applied to them. For example adding 1 and -1 in nibbles: 0001 + 1111, the bits overflow out of the register, leaving behind 0000.
For a gentle introduction, the wonderful Computerphile have produced a video on the subject.
The question is 'What is “two's complement”?'
The simple answer for those wanting to understand it theoretically (and me seeking to complement the other more practical answers): 2's complement is the representation for negative integers in the dual system that does not require additional characters, such as + and -.
Two's complement of a given number is the number got by adding 1 with the ones' complement of the number.
Suppose, we have a binary number: 10111001101
Its 1's complement is: 01000110010
And its two's complement will be: 01000110011
Reference: Two's Complement (Thomas Finley)
I invert all the bits and add 1. Programmatically:
// In C++11
int _powers[] = {
1,
2,
4,
8,
16,
32,
64,
128
};
int value = 3;
int n_bits = 4;
int twos_complement = (value ^ ( _powers[n_bits]-1)) + 1;
You can also use an online calculator to calculate the two's complement binary representation of a decimal number: http://www.convertforfree.com/twos-complement-calculator/
The simplest answer:
1111 + 1 = (1)0000. So 1111 must be -1. Then -1 + 1 = 0.
It's perfect to understand these all for me.

Need help understanding this line

thank you in advance for this basic question.
I am going through a tutorial and I see this line.
int a = (n & 8) / 8
This is supposed to identify whether the fourth bit from the right is a binary representation of 0 or 1. I understand the concept of bits etc, but I do not understand what mathematical equation (if any) this represents.
Would anyone care to explain how this would be written in a mathematical equation? Also, please let me know if i am missing anything else in my understanding of this line. Thank you.
The expression ( n & 8 )
does Logical And of n with 1000 binary.
So that gets the 4th bit from right.
then dividing that by 8, shifts the value right 3 binary places. I.e. it moves the 4th bit to the rightmost place.
That is more clearly expressed as " >> 3"
So your overall expression would be something like:
(n AND 1000 ) >> 3
And that leaves the 4th bit of N in a temporary variable, as bit 0 (rightmost bit).
All the other bits will be zero because of the AND.
8 in decimal is 1000 in binary
so if you do bitwise AND with any number
n & 8
it will stay 8 only if the 4th bit is 1 and
if you divide it by 8 again it will return 1, zero otherwise
For example
for 9 (1001)
9 & 8
would be
1001
& 1000
------
1000
Now for the case where forth bit is 0
for 7 (0111)
7 & 8
would be
0111
& 1000
-----
0000
int a = (n & 8) / 8;
The n & 8 applys a logical AND mask to the 4th bit of n;
n: 11001010 // example value
8: 00001000
result: 00001000
Dividing that number by 8 brings the result to the lowest bit :
result: 00000001
Dividing a number by 2^n shifts the numbers n bits to the right (in the same way that multiplying by 2^n shifts bits to the left).
The result is assigned to variable a, which now contains 0 or 1, depending on the value of the 4th bit.
Bitwise operator works on bits and performs bit-by-bit operation. Assume if a = 60; and b = 13; now in binary format they will be as follows:
a = 0011 1100
b = 0000 1101
a&b=0000 1100
then a&b is also an integer which is further divided by 8 in your example.

Why does a signed shift right bring in 1s instead of just holding a 1 in the most significant spot?

...and bring in 0s.
For example:
1111 0110 >> 2
gives
11 1111 01
according to some notes.
Why are 1s brought in? Why not just hold the left most bit at 1 and bring 0s in?
Isn't shifting to the right sort of like dividing by 2? If so shouldn't 0s be brought in?
You want a right shift to be the same as dividing by 2. Assuming that’s a signed 8-bit quantity, this is -128:
1000 0000
This is -64:
1100 0000
So this is a counterexample to your suggestion.
It's called "sign extension" and it's there exactly in order to have the effect of dividing by two. If the leftmost bit became 0, the sign of the originally negative number would change into positive. If only the leftmost bit was kept one, and further bits changed to zero, the result would have nothing to do with division by two.
If you want zeros to be prepended on the left, there is a special operator for that: >>>.
NB Java has only one unsigned integer quantity, and it is char. On this data type, >> works by prepending zeros on the left.

Categories