On Page 140 of Programming Pearls, 2nd Edition, Jon proposed an implementation of sets with bit vectors.
We'll turn now to two final structures that exploit the fact that our sets represent integers. Bit vectors are an old friend from Column 1. Here are their private data and functions:
enum { BITSPERWORD = 32, SHIFT = 5, MASK = 0x1F };
int n, hi, *x;
void set(int i) { x[i>>SHIFT] |= (1<<(i & MASK)); }
void clr(int i) { x[i>>SHIFT] &= ~(1<<(i & MASK)); }
int test(int i) { return x[i>>SHIFT] &= (1<<(i & MASK)); }
As I gathered, the central idea of a bit vector to represent an integer set, as described in Column 1, is that the i-th bit is turned on if and only if the integer i is in the set.
But I am really at a loss at the algorithms involved in the above three functions. And the book doesn't give an explanation.
I can only get that i & MASK is to get the lower 5 bits of i, while i>>SHIFT is to move i 5 bits toward the right.
Anybody would elaborate more on these algorithms? Bit operations always seem a myth to me, :(
Bit Fields and You
I'll use a simple example to explain the basics. Say you have an unsigned integer with four bits:
[0][0][0][0] = 0
You can represent any number here from 0 to 15 by converting it to base 2. Say we have the right end be the smallest:
[0][1][0][1] = 5
So the first bit adds 1 to the total, the second adds 2, the third adds 4, and the fourth adds 8. For example, here's 8:
[1][0][0][0] = 8
So What?
Say you want to represent a binary state in an application-- if some option is enabled, if you should draw some element, and so on. You probably don't want to use an entire integer for each one of these- it'd be using a 32 bit integer to store one bit of information. Or, to continue our example in four bits:
[0][0][0][1] = 1 = ON
[0][0][0][0] = 0 = OFF //what a huge waste of space!
(Of course, the problem is more pronounced in real life since 32-bit integers look like this:
[0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0] = 0
The answer to this is to use a bit field. We have a collection of properties (usually related ones) which we will flip on and off using bit operations. So, say, you might have 4 different lights on a piece of hardware that you want to be on or off.
3 2 1 0
[0][0][0][0] = 0
(Why do we start with light 0? I'll explain this in a second.)
Note that this is an integer, and is stored as an integer, but is used to represent multiple states for multiple objects. Crazy! Say we turn lights 2 and 1 on:
3 2 1 0
[0][1][1][0] = 6
The important thing you should note here: There's probably no obvious reason why lights 2 and 1 being on should equal six, and it may not be obvious how we would do anything with this scheme of information storage. It doesn't look more obvious if you add more bits:
3 2 1 0
[1][1][1][0] = 0xE \\what?
Why do we care about this? Do we have exactly one state for each number between 0 and 15?How are we going to manage this without some insane series of switch statements? Ugh...
The Light at the End
So if you've worked with binary arithmetic a bit before, you might realize that the relationship between the numbers on the left and the numbers on the right is, of course, base 2. That is:
1*(23) + 1*(22) + 1*(21) +0 *(20) = 0xE
So each light is present in the exponent of each term of the equation. If the light is on, there is a 1 next to its term- if the light is off, there is a zero. Take the time to convince yourself that there is exactly one integer between 0 and 15 that corresponds to each state in this numbering scheme.
Bit operators
Now that we have this done, let's take a second to see what bitshifting does to integers in this setup.
[0][0][0][1] = 1
When you shift bits to the left or the right in an integer, it literally moves the bits left and right. (Note: I 100% disavow this explanation for negative numbers! There be dragons!)
1<<2 = 4
[0][1][0][0] = 4
4>>1 = 2
[0][0][1][0] = 2
You will encounter similar behavior when shifting numbers represented with more than one bit. Also, it shouldn't be hard to convince yourself that x>>0 or x<<0 is just x. Doesn't shift anywhere.
This probably explains the naming scheme of the Shift operators to anyone who wasn't familiar with them.
Bitwise operations
This representation of numbers in binary can also be used to shed some light on the operations of bitwise operators on integers. Each bit in the first number is xor-ed, and-ed, or or-ed with its fellow number. Take a second to venture to wikipedia and familiarize yourself with the function of these Boolean operators - I'll explain how they function on numbers but I don't want to rehash the general idea in great detail.
...
Welcome back! Let's start by examining the effect of the OR (|) operator on two integers, stored in four bit.
OR OPERATOR ON:
[1][0][0][1] = 0x9
[1][1][0][0] = 0xC
________________
[1][1][0][1] = 0xD
Tough! This is a close analogue to the truth table for the boolean OR operator. Notice that each column ignores the adjacent columns and simply fills in the result column with the result of the first bit and the second bit OR'd together. Note also that the value of anything or'd with 1 is 1 in that particular column. Anything or'd with zero remains the same.
The table for AND (&) is interesting, though somewhat inverted:
AND OPERATOR ON:
[1][0][0][1] = 0x9
[1][1][0][0] = 0xC
________________
[1][0][0][0] = 0x8
In this case we do the same thing- we perform the AND operation with each bit in a column and put the result in that bit. No column cares about any other column.
Important lesson about this, which I invite you to verify by using the diagram above: anything AND-ed with zero is zero. Also, equally important- nothing happens to numbers that are AND-ed with one. They stay the same.
The final table, XOR, has behavior which I hope you all find predictable by now.
XOR OPERATOR ON:
[1][0][0][1] = 0x9
[1][1][0][0] = 0xC
________________
[0][1][0][1] = 0x5
Each bit is being XOR'd with its column, yadda yadda, and so on. But look closely at the first row and the second row. Which bits changed? (Half of them.) Which bits stayed the same? (No points for answering this one.)
The bit in the first row is being changed in the result if (and only if) the bit in the second row is 1!
The one lightbulb example!
So now we have an interesting set of tools we can use to flip individual bits. Let's go back to the lightbulb example and focus only on the first lightbulb.
0
[?] \\We don't know if it's one or zero while coding
We know that we have an operation that can always make this bit equal to one- the OR 1 operator.
0|1 = 1
1|1 = 1
So, ignoring the rest of the bulbs, we could do this
4_bit_lightbulb_integer |= 1;
and know for sure that we did nothing but set the first lightbulb to ON.
3 2 1 0
[0][0][0][?] = 0 or 1? \\4_bit_lightbulb_integer
[0][0][0][1] = 1
________________
[0][0][0][1] = 0x1
Similarly, we can AND the number with zero. Well- not quite zero- we don't want to affect the state of the other bits, so we will fill them in with ones.
I'll use the unary (one-argument) operator for bit negation. The ~ (NOT) bitwise operator flips all of the bits in its argument. ~(0X1):
[0][0][0][1] = 0x1
________________
[1][1][1][0] = 0xE
We will use this in conjunction with the AND bit below.
Let's do 4_bit_lightbulb_integer & 0xE
3 2 1 0
[0][1][0][?] = 4 or 5? \\4_bit_lightbulb_integer
[1][1][1][0] = 0xE
________________
[0][1][0][0] = 0x4
We're seeing a lot of integers on the right-hand-side which don't have any immediate relevance. You should get used to this if you deal with bit fields a lot. Look at the left-hand side. The bit on the right is always zero and the other bits are unchanged. We can turn off light 0 and ignore everything else!
Finally, you can use the XOR bit to flip the first bit selectively!
3 2 1 0
[0][1][0][?] = 4 or 5? \\4_bit_lightbulb_integer
[0][0][0][1] = 0x1
________________
[0][1][0][*] = 4 or 5?
We don't actually know what the value of * is now- just that flipped from whatever ? was.
Combining Bit Shifting and Bitwise operations
The interesting fact about these two operations is when taken together they allow you to manipulate selective bits.
[0][0][0][1] = 1 = 1<<0
[0][0][1][0] = 2 = 1<<1
[0][1][0][0] = 4 = 1<<2
[1][0][0][0] = 8 = 1<<3
Hmm. Interesting. I'll mention the negation operator here (~) as it's used in a similar way to produce the needed bit values for ANDing stuff in bit fields.
[1][1][1][0] = 0xE = ~(1<<0)
[1][1][0][1] = 0xD = ~(1<<1)
[1][0][1][1] = 0xB = ~(1<<2)
[0][1][1][1] = 0X7 = ~(1<<3)
Are you seeing an interesting relationship between the shift value and the corresponding lightbulb position of the shifted bit?
The canonical bitshift operators
As alluded to above, we have an interesting, generic method for turning on and off specific lights with the bit-shifters above.
To turn on a bulb, we generate the 1 in the right position using bit shifting, and then OR it with the current lightbulb positions. Say we want to turn on light 3, and ignore everything else. We need to get a bit shifting operation that ORs
3 2 1 0
[?][?][?][?] \\all we know about these values at compile time is where they are!
and 0x8
[1][0][0][0] = 0x8
Which is easy, thanks to bitshifting! We'll pick the number of the light and switch the value over:
1<<3 = 0x8
and then:
4_bit_lightbulb_integer |= 0x8;
3 2 1 0
[1][?][?][?] \\the ? marks have not changed!
And we can guarantee that the bit for the 3rd lightbulb is set to 1 and that nothing else has changed.
Clearing a bit works similarly- we'll use the negated bits table above to, say, clear light 2.
~(1<<2) = 0xB = [1][0][1][1]
4_bit_lightbulb_integer & 0xB:
3 2 1 0
[?][?][?][?]
[1][0][1][1]
____________
[?][0][?][?]
The XOR method of flipping bits is the same idea as the OR one.
So the canonical methods of bit switching are this:
Turn on the light i:
4_bit_lightbulb_integer|=(1<<i)
Turn off light i:
4_bit_lightbulb_integer&=~(1<<i)
Flip light i:
4_bit_lightbulb_integer^=(1<<i)
Wait, how do I read these?
In order to check a bit we can simply zero out all of the bits except for the one we care about. We'll then check to see if the resulting value is greater than zero- since this is the only value that could possibly be nonzero, it will make the entire integer nonzero if and only if it is nonzero. For example, to check bit 2:
1<<2:
[0][1][0][0]
4_bit_lightbulb_integer:
[?][?][?][?]
1<<2 & 4_bit_lightbulb_integer:
[0][?][0][0]
Remember from the previous examples that the value of ? didn't change. Remember also that anything AND 0 is 0. So, we can say for sure that if this value is greater than zero, the switch at position 2 is true and the lightbulb is zero. Similarly, if the value is off, the value of the entire thing will be zero.
(You can alternately shift the entire value of 4_bit_lightbulb_integer over by i bits and AND it with 1. I don't remember off the top of my head if one is faster than the other but I doubt it.)
So the canonical checking function:
Check if bit i is on:
if (4_bit_lightbulb_integer & 1<<i) {
\\do whatever
}
The specifics
Now that we have a complete set of tools for bitwise operations, we can look at the specific example here. This is basically the same idea- except a much more concise and powerful way of executing it. Let's look at this function:
void set(int i) { x[i>>SHIFT] |= (1<<(i & MASK)); }
From the canonical implementation I'm going to make a guess that this is trying to set some bits to 1! Let's take an integer and look at what's going on here if i feed the value 0x32 (50 in decimal) into i:
x[0x32>>5] |= (1<<(0x32 & 0x1f))
Well, that's a mess.. let's dissect this operation on the right. For convenience, pretend there are 24 more irrelevant zeros, since these are both 32 bit integers.
...[0][0][0][1][1][1][1][1] = 0x1F
...[0][0][1][1][0][0][1][0] = 0x32
________________________
...[0][0][0][1][0][0][1][0] = 0x12
It looks like everything is being cut off at the boundary on top where 1s turn into zeros. This technique is called Bit Masking. Interestingly, the boundary here restricts the resulting values to be between 0 and 31... Which is exactly the number of bit positions we have for a 32 bit integer!
x[0x32>>5] |= (1<<(0x12))
Let's look at the other half.
...[0][0][1][1][0][0][1][0] = 0x32
Shift five bits to the right:
...[0][0][0][0][0][0][0][1] = 0x01
Note that this transformation exactly destroyed all information from the first part of the function- we have 32-5 = 27 remaining bits which could be nonzero. This indicates which of 227 integers in the array of integers are selected. So the simplified equation is now:
x[1] |= (1<<0x12)
This just looks like the canonical bit-setting operation! We've just chosen
So the idea is to use the first 27 bits to pick an integer to shift and the last five bits indicate which bit of the 32 in that integer to shift.
The key to understanding what's going on is to recognize that BITSPERWORD = 2SHIFT. Thus, x[i>>SHIFT] finds which 32-bit element of the array x has the bit corresponding to i. (By shifting i 5 bits to the right, you're simply dividing by 32.) Once you have located the correct element of x, the lower 5 bits of i can then be used to find which particular bit of x[i>>SHIFT] corresponds to i. That's what i & MASK does; by shifting 1 by that number of bits, you move the bit corresponding to 1 to the exact position within x[i>>SHIFT] that corresponds to the ith bit in x.
Here's a bit more of an explanation:
Imagine that we want capacity for N bits in our bit vector. Since each int holds 32 bits, we will need (N + 31) / 32 int values for our storage (that is, N/32 rounded up). Within each int value, we will adopt the convention that bits are ordered from least significant to most significant. We will also adopt the convention that the first 32 bits of our vector are in x[0], the next 32 bits are in x[1], and so forth. Here's the memory layout we are using (showing the bit index in our bit vector corresponding to each bit of memory):
+----+----+-------+----+----+----+
x[0]: | 31 | 30 | . . . | 02 | 01 | 00 |
+----+----+-------+----+----+----+
x[1]: | 63 | 62 | . . . | 34 | 33 | 32 |
+----+----+-------+----+----+----+
etc.
Our first step is to allocate the necessary storage capacity:
x = new int[(N + BITSPERWORD - 1) >> SHIFT]
(We could make provision for dynamically expanding this storage, but that would just add complexity to the explanation.)
Now suppose we want to access bit i (either to set it, clear it, or just to know its current value). We need to first figure out which element of x to use. Since there are 32 bits per int value, this is easy:
subscript for x = i / 32
Making use of the enum constants, the x element we want is:
x[i >> SHIFT]
(Think of this as a 32-bit-wide window into our N-bit vector.) Now we have to find the specific bit corresponding to i. Looking at the memory layout, it's not hard to figure out that the first (rightmost) bit in the window corresponds to bit index 32 * (i >> SHIFT). (The window starts afteri >> SHIFT slots in x, and each slot has 32 bits.) Since that's the first bit in the window (position 0), then the bit we're interested in is is at position
i - (32 * (i >> SHIFT))
in the windows. With a little experimenting, you can convince yourself that this expression is always equal to i % 32 (actually, that's one definition of the mod operator) which, in turn, is always equal to i & MASK. Since this last expression is the fastest way to calculate what we want, that's what we'll use.
From here, the rest is pretty simple. We start with a single bit in the least-significant position of the window (that is, the constant 1), and move it to the left by i & MASK bits to get it to the position in the window corresponding to bit i in the bit vector. This is where the expression
1 << (i & MASK)
comes from. With the bit now moved to where we want it, we can use this as a mask to set, clear, or query the value of the bit at that position in x[i>>SHIFT] and we know that we're actually setting, clearing, or querying the value of bit i in our bit vector.
If you store your bits in an array of n words you can imagine them to be layed out as a matrix with n rows and 32 columns (BITSPERWORD):
3 0
1 0
0 xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx
1 xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx
2 xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx
....
n xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx
To get the k-th bit you divide k by 32. The (integer) result will give you the row (word) the bit is in, the reminder will give you which bit is within the word.
Dividing by 2^p can be done simply by shifting p postions to the right. The reminder can be obtained by getting the p rightmost bits (i.e the bitwise AND with (2^p - 1)).
In C terms:
#define div32(k) ((k) >> 5)
#define mod32(k) ((k) & 31)
#define word_the_bit_is_in(k) div32(k)
#define bit_within_word(k) mod32(k)
Hope it helps.
Related
I have a number X , I want to check the number of powers of 2 it have ?
For Ex
N=7 ans is 2 , 2*2
N=20 ans is 4, 2*2*2*2
Similar I want to check the next power of 2
For Ex:
N=14 Ans=16
Is there any Bit Hack for this without using for loops ?
Like we are having a one line solution to check if it's a power of 2 X&(X-1)==0,similarly like that ?
GCC has a built-in instruction called __builtin_clz() that returns the number of leading zeros in an integer. So for example, assuming a 32-bit int, the expression p = 32 - __builtin_clz(n) will tell you how many bits are needed to store the integer n, and 1 << p will give you the next highest power of 2 (provided p<32, of course).
There are also equivalent functions that work with long and long long integers.
Alternatively, math.h defines a function called frexp() that returns the base-2 exponent of a double-precision number. This is likely to be less efficient because your integer will have to be converted to a double-precision value before it is passed to this function.
A number is power of two if it has only single '1' in its binary value. For example, 2 = 00000010, 4 = 00000100, 8 = 00001000 and so on. So you can check it using counting the no. of 1's in its bit value. If count is 1 then the number is power of 2 and vice versa.
You can take help from here and here to avoid for loops for counting set bits.
If count is not 1 (means that Value is not power of 2) then take position of its first set bit from MSB and the next power of 2 value to this number is the value having only set bit at position + 1. For example, number 3 = 00000011. Its first set bit from MSB is 2nd bit. Therefore the next power of 2 number is a value having only set bit at 3rd position. i.e. 00000100 = 4.
A double gutter in poker is when you have 2 gut shots to a straight.
For example, if the board is 268 and you have 45, a 3 (23456) or a 7 (45678) will complete a straight for you.
Given board[] and hand[], what's an efficient algorithm for returning true if you have a double gutter and false if not?
Here's an algorithm that can do this with bit vectors.
First, create a bit vector of 14 bits, say cards (to include low and high ace) and set each bit for each element in hand[] and board[].
Next, generate 5 pairs of masks (mask, gutter) for the first element in hand[] such that ((cards & mask) ^ gutter)==0 if you have a double gutter. E.g., if one of your cards is 7, the (mask, gutter) pairs would be (assuming MSB->LSB order):
01111111000000, 01011101000000
00000001111111, 00000001011101
00000111111100, 00000101110100
00001111111000, 00001011101000
00011111110000, 00010111010000
I.e., you have 2 double gutters with 7 on either end and 3 with 7 in the middle.
Now, calculate ((cards & mask) ^ gutter) for each of the 5 pairs. If any is 0 you have a double gutter.
EDIT: turns out you only need to test 5 masks.
My guess:
Make a bool array (or bit field) for every card value
For every card in board[] and hand[], set the corresponding entry to true
Check all possibilities for a straight; since you don't care about colors, options are very limited (i.e. 23456, 34567 etc.) - if exactly one card is missing from a possible straight, add it to a set (which cannot contain duplicates).
Return true iff the set contains exactly 2 entries.
Alternatively, do one experiment for any value not yet on the board, checking if you have a straight if you add that value and count the ones for which this happens.
All the algorithms are constant time (since the entire situation is limited), and they don't take a really large effort.
Here's my approach using bit-wise operations :
vec = 0
map hand[] and board[] to bits of the number vec, then
gutter = 0
for i = 0 to 10
x = (vec & 0x1f) ^ 0x1f
if x one of 1,2,4,8,16
gutter++
if gutter >= 2
return true
vec = vec >> 1
return false
If there is any number in the range [0 .. 264] which can not be generated by any XOR composition of one or more numbers from a given set, is there a efficient method which prints at least one of the unreachable numbers, or terminates with the information, that there are no unreachable numbers?
Does this problem have a name? Is it similar to another problem or do you have any idea, how to solve it?
Each number can be treated as a vector in the vector space (Z/2)^64 over Z/2. You basically want to know if the vectors given span the whole space, and if not, to produce one not spanned (except that the span always includes the zero vector – you'll have to special case this if you really want one or more). This can be accomplished via Gaussian elimination.
Over this particular vector space, Gaussian elimination is pretty simple. Start with an empty set for the basis. Do the following until there are no more numbers. (1) Throw away all of the numbers that are zero. (2) Scan the lowest bits set of the remaining numbers (lowest bit for x is x & ~(x - 1)) and choose one with the lowest order bit set. (3) Put it in the basis. (4) Update all of the other numbers with that same bit set by XORing it with the new basis element. No remaining number has this bit or any lower order bit set, so we terminate after 64 iterations.
At the end, if there are 64 elements, then the subspace is everything. Otherwise, we went fewer than 64 iterations and skipped a bit: the number with only this bit on is not spanned.
To special-case zero: zero is an option if and only if we never throw away a number (i.e., the input vectors are independent).
Example over 4-bit numbers
Start with 0110, 0011, 1001, 1010. Choose 0011 because it has the ones bit set. Basis is now {0011}. Other vectors are {0110, 1010, 1010}; note that the first 1010 = 1001 XOR 0011.
Choose 0110 because it has the twos bit set. Basis is now {0011, 0110}. Other vectors are {1100, 1100}.
Choose 1100. Basis is now {0011, 0110, 1100}. Other vectors are {0000}.
Throw away 0000. We're done. We skipped the high order bit, so 1000 is not in the span.
As rap music points out you can think of the problem as finding a base in a vector space. However, it is not necessary to actually solve it completely, just to find if it is possible to do or not, and if not: give an example value (that is a binary vector) that can not be described in terms of the supplied set.
This can be done in O(n^2) in terms of the size of the input set. This should be compared to Gauss elimination which is O(n^3), http://en.wikipedia.org/wiki/Gaussian_elimination.
64 bits are no problem at all. With the example python code below 1000 bits with a set with 1000 random values from 0 to 2^1000-1 takes about a second.
Instead of performing Gauss elimination it's enough to find out if we can rewrite the matrix of all bits on triangular form, such as: (for the 4 bit version:)
original triangular
1110 14 1110 14
1011 11 111 7
111 7 11 3
11 3 1 1
1 1 0 0
The solution works like this: First all original values with the same most significant bit are places together in a list of lists. For our example:
[[14,11],[7],[3],[1],[]]
The last empty entry represents that there were no zeros in the original list. Now, take a value from the first entry and replace that entry with a list containing only that number:
[[14],[7],[3],[1],[]]
and then store the xor of the kept number with all the removed entries at the right place in the vector. For our case we have 14^11 = 5 so:
[[14],[7,5],[3],[1],[]]
The trick is that we do not need to scan and update all other values, just the values with the same most significant bit.
Now process the item 7,5 in the same way. Keep 7, add 7^5 = 2 to the list:
[[14],[7],[3,2],[1],[]]
Now 3,2 leaves [3] and adds 1 :
[[14],[7],[3],[1,1],[]]
And 1,1 leaves [1] and adds 0 to the last entry allowing values with no set bit:
[[14],[7],[3],[1],[0]]
If in the end the vector contains at least one number at each vector entry (as in our example) the base is complete and any number fits.
Here's the complete code:
# return leading bit index ir -1 for 0.
# example 1 -> 0
# example 9 -> 3
def leadbit(v):
# there are other ways, yes...
return len(bin(v))-3 if v else -1
def examinebits(baselist,nbitbuckets):
# index 1 is least significant bit.
# index 0 represent the value 0
bitbuckets=[[] for x in range(nbitbuckets+1)]
for j in baselist:
bitbuckets[leadbit(j)+1].append(j)
for i in reversed(range(len(bitbuckets))):
if bitbuckets[i]:
# leave just the first value of all in bucket i
bitbuckets[i],newb=[bitbuckets[i][0]],bitbuckets[i][1:]
# distribute the subleading values into their buckets
for ni in newb:
q=bitbuckets[i][0]^ni
lb=leadbit(q)+1
if lb:
bitbuckets[lb].append(q)
else:
bitbuckets[0]=[0]
else:
v=2**(i-1) if i else 0
print "bit missing: %d. Impossible value: %s == %d"%(i-1,bin(v),v)
return (bitbuckets,[i])
return (bitbuckets,[])
Example use: (8 bit)
import random
nbits=8
basesize=8
topval=int(2**nbits)
# random set of values to try:
basel=[random.randint(0,topval-1) for dummy in range(basesize)]
bl,ii=examinebits(basel,nbits)
bl is now the triangular list of values, up to the point where it was not possible (in that case). The missing bit (if any) is found in ii[0].
For the following tried set of values: [242, 242, 199, 197, 177, 177, 133, 36] the triangular version is:
base value: 10110001 177
base value: 1110110 118
base value: 100100 36
base value: 10000 16
first missing bit: 3 val: 8
( the below values where not completely processed )
base value: 10 2
base value: 1 1
base value: 0 0
The above list were printed like this:
for i in range(len(bl)):
bb=bl[len(bl)-i-1]
if ii and len(bl)-ii[0] == i:
print "example missing bit:" ,(ii[0]-1), "val:", 2**(ii[0]-1)
print "( the below values where not completely processed )"
if len(bb):
b=bb[0]
print ("base value: %"+str(nbits)+"s") %(bin(b)[2:]), b
I am trying to decipher some assembly code that involves multiple left rotations on an 8-bit binary number.
For reference, the code is:
lab: rol dl,1
rol dl,1
dec ecx
jnz lab
The dec and jnz isn't an issue, but is there to show that the 2 rols are executed several times.
What I am trying to do is figure out a mathematical equivalent of this code, such as a formula. I'm certainly not looking for a complete formula to tell me the whole code, but I would like to know if there is a formula that gives the equivalent (in denary) of a single left rotation.
I've tried figuring this out with a couple of different numbers, but cannot see a link between the two results. For example: if the start number is 115 it comes out as 220, but if the start number is 99 it comes out as 216.
Given your sample results, I assume we are treating the 8-bit quantity as unsigned.
The 7 low-order bits are shifted left, multiplying that part of the number by 2; and the high-order bit is swapped around to the beginning.
Thus, (x % 128) * 2 + (x / 128), using the usual integer div/mod operators.
Shifting a byte containing number X by one bit (position) left is equal to multiplying the number X by 2:
x << 1 <==> x = x * 2
I want to multiply two numbers, and detect if there was an overflow. What is the simplest way to do that?
Multiplying two 32 bit numbers results in a 64 bit answer, two 8s give a 16, etc. binary multiplication is simply shifting and adding. so if you had say two 32 bit operands and bit 17 set in operand A and any of the bits above 15 or 16 set in operand b you will overflow a 32 bit result. bit 17 shifted left 16 is bit 33 added to a 32.
So the question again is what are the size of your inputs and the size of your result, if the result is the same size then you have to find the most significant 1 of both operands add those bit locations if that result is bigger than your results space you will overflow.
EDIT
Yes multiplying two 3 bit numbers will result in either a 5 bit number or 6 bit number if there is a carry in the add. Likewise a 2 bit and 5 bit can result in 6 or 7 bits, etc. If the reason for this question posters question is to see if you have space in your result variable for an answer then this solution will work and is relatively fast for most languages on most processors. It can be significantly faster on some and significantly slower on others. It is generically fast (depending on how it is implemented of course) to just look at the number of bits in the operands. Doubling the size of the largest operand is a safe bet if you can do it within your language or processor. Divides are downright expensive (slow) and most processors dont have one much less at an arbitrary doubling of operand sizes. The fastest of course is to drop to assembler do the multiply and look at the overflow bit (or compare one of the result registers with zero). If your processor cant do the multiply in hardware then it is going to be slow no matter what you do. I am guessing that asm is not the right answer to this post despite being by far the fastest and has the most accurate overflow status.
binary makes multiplication trivial compared to decimal, for example take the binary numbers
0b100 *
0b100
Just like decimal math in school you (can) start with the least significant bit on the lower operand and multiply it against all the locations in the upper operand, except with binary there are only two choices you multiply by zero meaning you dont have to add to the result, or you multiply by one which means you just shift and add, no actual multiplication is necessary like you would have in decimal.
000 : 0 * 100
000 : 0 * 100
100 : 1 * 100
Add up the columns and the answer is 0b10000
Same as decimal math a 1 in the hundreds column means copy the top number and add two zeros, it works the same in any other base as well. So 0b100 times 0b110 is 0b1000, a one in the second column over so copy and add a zero + 0b10000 a one in the third column over so copy and add two zeros = 0b11000.
This leads to looking at the most significant bits in both numbers. 0b1xx * 0b1xx guarantees a 1xxxx is added to the answer, and that is the largest bit location in the add, no other single inputs to the final add have that column populated or a more significant column populated. From there you need only more bit in case the other bits being added up cause a carry.
Which happens with the worst case all ones times all ones, 0b111 * 0b111
0b00111 +
0b01110 +
0b11100
This causes a carry bit in the addition resulting in 0b110001. 6 bits. a 3 bit operand times a 3 bit operand 3+3=6 6 bits worst case.
So size of the operands using the most significant bit (not the size of the registers holding the values) determines the worst case storage requirement.
Well, that is true assuming positive operands. If you consider some of these numbers to be negative it changes things but not by much.
Minus 4 times 5, 0b1111...111100 * 0b0000....000101 = -20 or 0b1111..11101100
it takes 4 bits to represent a minus 4 and 4 bits to represent a positive 5 (dont forget your sign bit). Our result required 6 bits if you stripped off all the sign bits.
Lets look at the 4 bit corner cases
-8 * 7 = -56
0b1000 * 0b0111 = 0b1001000
-1 * 7 = -7 = 0b1001
-8 * -8 = 64 = 0b01000000
-1 * -1 = 2 = 0b010
-1 * -8 = 8 = 0b01000
7 * 7 = 49 = 0b0110001
Lets say we count positive numbers as the most significant 1 plus one and negative the most significant 0 plus one.
-8 * 7 is 4+4=8 bits actual 7
-1 * 7 is 1+4=5 bits, actual 4 bits
-8 * -8 is 4+4=8 bits, actual 8 bits
-1 * -1 is 1+1=2 bits, actual 3 bits
-1 * -8 is 1+4=5 bits, actual 5 bits
7 * 7 is 4+4=8 bits, actual 7 bits.
So this rule works, with the exception of -1 * -1, you can see that I called a minus one one bit, for the plus one thing find the zero plus one. Anyway, I argue that if this were a 4 bit * 4 bit machine as defined, you would have 4 bits of result at least and I interpret the question as how may more than 4 bits do I need to safely store the answer. So this rule serves to answer that question for 2s complement math.
If your question was to accurately determine overflow and then speed is secondary, then, well it is going to be really really slow for some systems, for every multiply you do. If this is the question you are asking, to get some of the speed back you need to tune it a little better for the language and/or processor. Double up the biggest operand, if you can, and check for non-zero bits above the result size, or use a divide and compare. If you cant double the operand sizes, divide and compare. Check for zero before the divide.
Actually your question doesnt specify what size of overflow you are talking about either. Good old 8086 16 bit times 16 bit gives a 32 bit result (hardware), it can never overflow. What about some of the ARMs that have a multiply, 32 bit times 32 bit, 32 bit result, easy to overflow. What is the size of your operands for this question, are they the same size or are they double the input size? Are you willing to perform multiplies that the hardware cannot do (without overflowing)? Are you writing a compiler library and trying to determine if you can feed the operands to the hardware for speed or if you have to perform the math without a hardware multiply. Which is the kind of thing you get if you cast up the operands, the compiler library will try to cast the operands back down before doing the multiply, depending on the compiler and its library of course. And it will use the count the bit trick determine to use the hardware multiply or a software one.
My goal here was to show how binary multiply works in a digestible form so you can see how much maximum storage you need by finding the location of a single bit in each operand. Now how fast you can find that bit in each operand is the trick. If you were looking for minimum storage requirements not maximum that is a different story because involves every single one of the significant bits in both operands not just one bit per operand, you have to do the multiply to determine minimum storage. If you dont care about maximum or minimum storage you have to just do the multiply and look for non zeros above your defined overflow limit or use a divide if you have the time or hardware.
Your tags imply you are not interested in floating point, floating point is a completely different beast, you cannot apply any of these fixed point rules to floating point, they DO NOT work.
Check if one is less than a maximum value divided by the other. (All values are taken as absolute).
2's complementness hardly has anything to do with it, since the multiplication overflows if x*(2n - x)>2M, which is equal to (x*2n - x2)>2M, or x2 < (x*2n - 2M), so you'll have to compare overflowing numbers anyway (x2 may overflow, while result may not).
If your number are not from the largest integral data type, then you might just cast them up, multiply and compare with the maximum of the number's original type. E.g. in Java, when multiplying two int, you can cast them to long and compare the result to Integer.MAX_VALUE or Integer.MIN_VALUE (depending on sign combination), before casting the result down to int.
If the type already is the largest, then check if one is less than the maximum value divided by the other. But do not take the absolute value! Instead you need separate comparison logic for each of the sign combinations negneg, pospos and posneg (negpos can obviously be reduced to posneg, and pospos might be reduced to neg*neg). First test for 0 arguments to allow safe divisions.
For actual code, see the Java source of MathUtils class of the commons-math 2, or ArithmeticUtils of commons-math 3. Look for public static long mulAndCheck(long a, long b). The case for positive a and b is
// check for positive overflow with positive a, positive b
if (a <= Long.MAX_VALUE / b) {
ret = a * b;
} else {
throw new ArithmeticException(msg);
}
I want to multiply two (2's complement) numbers, and detect if there was an overflow. What is the simplest way to do that?
Various languages do not specify valid checking for overflow after it occurs and so prior tests are required.
With some types, a wider integer type may not exist, so a general solution should limit itself to a single type.
The below (Ref) only requires compares and known limits to the integer range. It returns 1 if a product overflow will occur, else 0.
int is_undefined_mult1(int a, int b) {
if (a > 0) {
if (b > 0) {
return a > INT_MAX / b; // a positive, b positive
}
return b < INT_MIN / a; // a positive, b not positive
}
if (b > 0) {
return a < INT_MIN / b; // a not positive, b positive
}
return a != 0 && b < INT_MAX / a; // a not positive, b not positive
}
Is this the simplest way?
Perhaps, yet it is complete and handle all cases known to me - including rare non-2's complement.
Alternatives to Pavel Shved's solution ...
If your language of choice is assembler, then you should be able to check the overflow flag. If not, you could write a custom assembler routine that sets a variable if the overflow flag was set.
If this is not acceptable, you can find the most signficant set bit of both values (absolutes). If the sum exceeds the number of bits in the integer (or unsigned) then you will have an overflow if they are multiplied together.
Hope this helps.
In C, here's some maturely optimized code that handles the full range of corner cases:
int
would_mul_exceed_int(int a, int b) {
int product_bits;
if (a == 0 || b == 0 || a == 1 || b == 1) return (0); /* always okay */
if (a == INT_MIN || b == INT_MIN) return (1); /* always underflow */
a = ABS(a);
b = ABS(b);
product_bits = significant_bits_uint((unsigned)a);
product_bits += significant_bits_uint((unsigned)b);
if (product_bits == BITS(int)) { /* cases where the more expensive test is required */
return (a > INT_MAX / b); /* remember that IDIV and similar are very slow (dozens - hundreds of cycles) compared to bit shifts, adds */
}
return (product_bits > BITS(int));
}
Full example with test cases here
The benefit of the above approach is it doesn't require casting up to a larger type, so the approach could work on larger integer types.