I have the following code for self learning:
#include <iostream>
using namespace std;
struct bitfields{
unsigned field1: 3;
unsigned field2: 4;
unsigned int k: 4;
};
int main(){
bitfields field;
field.field1=8;
field.field2=1e7;
field.k=18;
cout<<field.k<<endl;
cout<<field.field1<<endl;
cout<<field.field2<<endl;
return 0;
}
I know that unsigned int k:4 means that k is 4 bits wide, or a maximum value of 15, and the result is the following.
2
0
1
For example, filed1 can be from 0 to 7 (included), field2 and k from 0 to 15. Why such a result? Maybe it should be all zero?
You're overflowing your fields. Let's take k as an example, it's 4 bits wide. It can hold values, as you say, from 0 to 15, in binary representation this is
0 -> 0000
1 -> 0001
2 -> 0010
3 -> 0011
...
14 -> 1110
15 -> 1111
So when you assign 18, having binary representation
18 -> 1 0010 (space added between 4th and 5th bit for clarity)
k can only hold the lower four bits, so
k = 0010 = 2.
The equivalent holds true for the rest of your fields as well.
You have these results because the assignments overflowed each bitfield.
The variable filed1 is 3 bits, but 8 takes 4 bits to present (1000). The lower three bits are all zero, so filed1 is zero.
For filed2, 17 is represented by 10001, but filed2 is only four bits. The lower four bits represent the value 1.
Finally, for k, 18 is represented by 10010, but k is only four bits. The lower four bits represent the value 2.
I hope that helps clear things up.
In C++ any unsigned type wraps around when you hit its ceiling[1]. When you define a bitfield of 4 bits, then every value you store is wrapped around too. The possible values for a bitfield of size 4 are 0-15. If you store '17', then you wrap to '1', for '18' you go one more to '2'.
Mathematically, the wrapped value is the original value modulo the number of possible values for the destination type:
For the bitfield of size 4 (2**4 possible values):
18 % 16 == 2
17 % 16 == 1
For the bitfield of size 3 (2**3 possible values):
8 % 8 == 0.
[1] This is not true for signed types, where it is undefined what happens then.
Related
Given 15 random hexadecimal numbers (60 bits) where there is always at least 1 duplicate in every 20 bit run (5 hexdecimals).
What is the optimal way to compress the bytes?
Here are some examples:
01230 45647 789AA
D8D9F 8AAAF 21052
20D22 8CC56 AA53A
AECAB 3BB95 E1E6D
9993F C9F29 B3130
Initially I've been trying to use Huffman encoding on just 20 bits because huffman coding can go from 20 bits down to ~10 bits but storing the table takes more than 9 bits.
Here is the breakdown showing 20 bits -> 10 bits for 01230
Character Frequency Assignment Space Savings
0 2 0 2×4 - 2×1 = 6 bits
2 1 10 1×4 - 1×2 = 2 bits
1 1 110 1×4 - 1×3 = 1 bits
3 1 111 1×4 - 1×3 = 1 bits
I then tried to do huffman encoding on all 300 bits (five 60bit runs) and here is the mapping given the above example:
Character Frequency Assignment Space Savings
---------------------------------------------------------
a 10 101 10×4 - 10×3 = 10 bits
9 8 000 8×4 - 8×3 = 8 bits
2 7 1111 7×4 - 7×4 = 0 bits
3 6 1101 6×4 - 6×4 = 0 bits
0 5 1100 5×4 - 5×4 = 0 bits
5 5 1001 5×4 - 5×4 = 0 bits
1 4 0010 4×4 - 4×4 = 0 bits
8 4 0111 4×4 - 4×4 = 0 bits
d 4 0101 4×4 - 4×4 = 0 bits
f 4 0110 4×4 - 4×4 = 0 bits
c 4 1000 4×4 - 4×4 = 0 bits
b 4 0011 4×4 - 4×4 = 0 bits
6 3 11100 3×4 - 3×5 = -3 bits
e 3 11101 3×4 - 3×5 = -3 bits
4 2 01000 2×4 - 2×5 = -2 bits
7 2 01001 2×4 - 2×5 = -2 bits
This yields a savings of 8 bits overall, but 8 bits isn't enough to store the huffman table. It seems because of the randomness of the data that the more bits you try to encode with huffman the less effective it works. Huffman encoding seemed to work best with 20 bits (50% reduction) but storing the table in 9 or less bits isnt possible AFAIK.
In the worst-case for a 60 bit string there are still at least 3 duplicates, the average case there are more than 3 duplicates (my assumption). As a result of at least 3 duplicates the most symbols you can have in a run of 60 bits is just 12.
Because of the duplicates plus the less than 16 symbols, I can't help but feel like there is some type of compression that can be used
If I simply count the number of 20-bit values with at least two hexadecimal digits equal, there are 524,416 of them. A smidge more than 219. So the most you could possibly save is a little less than one bit out of the 20.
Hardly seems worth it.
If I split your question in two parts:
How do I compress (perfect) random data: You can't. Every bit is some new entropy which can't be "guessed" by a compression algorithm.
How to compress "one duplicate in five characters": There are exactly 10 options where the duplicate can be (see table below). This is basically the entropy. Just store which option it is (maybe grouped for the whole line).
These are the options:
AAbcd = 1 AbAcd = 2 AbcAd = 3 AbcdA = 4 (<-- cases where first character is duplicated somewhere)
aBBcd = 5 aBcBd = 6 aBcdB = 7 (<-- cases where second character is duplicated somewhere)
abCCd = 8 abCdC = 9 (<-- cases where third character is duplicated somewhere)
abcDD = 0 (<-- cases where last characters are duplicated)
So for your first example:
01230 45647 789AA
The first one (01230) is option 4, the second 3 and the third option 0.
You can compress this by multiplying each consecutive by 10: (4*10 + 3)*10 + 0 = 430
And uncompress it by using divide and modulo: 430%10=0, (430/10)%10=3, (430/10/10)%10=4. So you could store your number like that:
1AE 0123 4567 789A
^^^ this is 430 in hex and requires only 10 bit
The maximum number for the three options combined is 1000, so 10 bit are enough.
Compared to storing these 3 characters normally you save 2 bit. As someone else already commented - this is probably not worth it. For the whole line it's even less: 2 bit / 60 bit = 3.3% saved.
If you want to get rid of the duplicates first, do this, then look at the links at the bottom of the page. If you don't want to get rid of the duplicates, then still look at the links at the bottom of the page:
Array.prototype.contains = function(v) {
for (var i = 0; i < this.length; i++) {
if (this[i] === v) return true;
}
return false;
};
Array.prototype.unique = function() {
var arr = [];
for (var i = 0; i < this.length; i++) {
if (!arr.contains(this[i])) {
arr.push(this[i]);
}
}
return arr;
}
var duplicates = [1, 3, 4, 2, 1, 2, 3, 8];
var uniques = duplicates.unique(); // result = [1,3,4,2,8]
console.log(uniques);
Then you would have shortened your code that you have to deal with. Then you might want to check out Smaz
Smaz is a simple compression library suitable for compressing strings.
If that doesn't work, then you could take a look at this:
http://ed-von-schleck.github.io/shoco/
Shoco is a C library to compress and decompress short strings. It is very fast and easy to use. The default compression model is optimized for english words, but you can generate your own compression model based on your specific input data.
Let me know if it works!
This question already has answers here:
What is “two's complement”?
(24 answers)
Closed 7 years ago.
I'm trying to understand why INT_MIN is equal to -2^31 - 1 and not just -2^31.
My understanding is that an int is 4 bytes = 32 bits. Of these 32 bits, I assume 1 bit is used for the +/- sign, leaving 31 bits for the actual value. As such, INT_MAX is equal to 2^31-1 = 2147483647. On the other hand, why is INT_MIN equal to -2^31 = -2147483648? Wouldn't this exceed the '4 bytes' allotted for int? Based on my logic, I would have expected INT_MIN to equal -2^31 = -2147483647
Most modern systems use two's complement to represent signed integer data types. In this representation, one state in the positive side is used up to represent zero, hence one positive value lesser than the negatives. In fact this is one of the prime advantage this system has over the sign-magnitude system, where zero has two representations, +0 and -0. Since zero has only one representation in two's complement, the other state, now free, is used to represent one more number.
Let's take a small data type, say 4 bits wide, to understand this better. The number of possible states with this toy integer type would be 2⁴ = 16 states. When using two's complement to represent signed numbers, we would have 8 negative and 7 positive numbers and zero; in sign-magnitude system, we'd get two zeros, 7 positive and 7 negative numbers.
Bin Dec
0000 = 0
0001 = 1
0010 = 2
0011 = 3
0100 = 4
0101 = 5
0110 = 6
0111 = 7
1000 = -8
1001 = -7
1010 = -6
1011 = -5
1100 = -4
1101 = -3
1110 = -2
1111 = -1
I think you are confused since you are imagining that sign-magnitude representation is used for signed numbers; although this is also allowed by the language standards, this system is very less likely to be implemented as two's complement system is significantly a better representation.
As of C++20, only two's complement is allowed for signed integers; source.
I don't understand why an n-bit 2C system number can be extended to an (n+1)-bit 2C system number by making bit bn = bn−1, that is, extending to (n+1) bits by replicating the sign bit.
This works because of the way we calculate the value of a binary integer.
Working right to left, the sum of each bit_i * 2 ^ i,
where
i is the range 0 to n
n is the number of bits
Because each subsequent 0 bit will not increase the magnitude of the sum, it is the appropriate value to pad a smaller value into a wider bit field.
For example, using the number 5:
4 bit: 0101
5 bit: 00101
6 bit: 000101
7 bit 0000101
8 bit: 00000101
The opposite is true for negative numbers in a two's compliment system.
Remember you calculate two's compliment by first calculating the one's compliment and then adding 1.
Invert the value from the previous example to get -5:
4 bit: 0101 (invert)-> 1010 + 1 -> 1011
5 bit: 00101 (invert)-> 11010 + 1 -> 11011
6 bit: 000101 (invert)-> 111010 + 1 -> 111011
7 bit: 0000101 (invert)-> 1111010 + 1 -> 1111011
8 bit: 00000101 (invert)-> 11111010 + 1 -> 11111011
Here's the implementation of std::bitset::count with MSVC 2010:
size_t count() const
{ // count number of set bits
static char _Bitsperhex[] = "\0\1\1\2\1\2\2\3\1\2\2\3\2\3\3\4";
size_t _Val = 0;
for (int _Wpos = _Words; 0 <= _Wpos; --_Wpos)
for (_Ty _Wordval = _Array[_Wpos]; _Wordval != 0; _Wordval >>= 4)
_Val += _Bitsperhex[_Wordval & 0xF];
return (_Val);
}
Can someone explain to me how this is working? what's the trick with _Bitsperhex?
_Bitsperhex contains the number of set bits in a hexadecimal digit, indexed by the digit.
digit: 0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111
value: 0 1 1 2 1 2 2 3 1 2 2 3 2 3 3 4
index: 0 1 2 3 4 5 6 7 8 9 A B C D E F
The function retrieves one digit at a time from the value it's working with by ANDing with 0xF (binary 1111), looks up the number of set bits in that digit, and sums them.
_Bitsperhex is a 16 element integer array that maps a number in [0..15] range to the number of 1 bits in the binary representation of that number. For example, _Bitsperhex[3] is equal to 2, which is the number of 1 bits in the binary representation of 3.
The rest is easy: each multi-bit word in internal array _Array is interpreted as a sequence of 4-bit values. Each 4-bit value is fed through the above _Bitsperhex table to count the bits.
It is a slightly different implementation of the lookup table-based method described here: http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetTable. At the link they use a table of 256 elements and split 32-bit words into four 8-bit values.
I am using 2' complement to represent a negative number in binary form
Case 1:number -5
According to the 2' complement technique:
Convert 5 to the binary form:
00000101, then flip the bits
11111010, then add 1
00000001
=> result: 11111011
To make sure this is correct, I re-calculate to decimal:
-128 + 64 + 32 + 16 + 8 + 2 + 1 = -5
Case 2: number -240
The same steps are taken:
11110000
00001111
00000001
00010000 => recalculate this I got 16, not -240
I am misunderstanding something?
The problem is that you are trying to represent 240 with only 8 bits. The range of an 8 bit signed number is -128 to 127.
If you instead represent it with 9 bits, you'll see you get the correct answer:
011110000 (240)
100001111 (flip the signs)
+
000000001 (1)
=
100010000
=
-256 + 16 = -240
Did you forget that -240 cannot be represented with 8 bits when it is signed ?
The lowest negative number you can express with 8 bits is -128, which is 10000000.
Using 2's complement:
128 = 10000000
(flip) = 01111111
(add 1) = 10000000
The lowest negative number you can express with N bits (with signed integers of course) is always - 2 ^ (N - 1).