I have integers with power 2^n where n = 0 to 5
Lets say I have integers such as 2^0=1, 2^1=2, 2^2=4, 2^3=8, 2^4=16, 2^5=32
now when I get sum 42 of some integers belonging to above mentioned integer (the integers
cannot repeat itself i.e 2+2 cannot be done )
How can I obtain the actual integers participated in calculating the value 42 which is 32,8,2
Format your number as binary notation, in this case for 42 it would be 101010, from the notation get the index of 1 (start from 0) , it's 5,3,1. and then you have 2^5 + 2^3 + 2^1
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Let's assume I have an N-bit stream of generated bits. (In my case 64kilobits.)
Whats the probability of finding a sequence of X "all true" bits, contained within a stream of N bits. Where X = (2 to 16), and N = (16 to 1000000), and X < N.
For example:
If N=16 and X=5, whats the likelyhood of finding 11111 within a 16-bit number.
Like this pseudo-code:
int N = 1<<16; // (64KB)
int X = 5;
int Count = 0;
for (int i = 0; i < N; i++) {
int ThisCount = ContiguousBitsDiscovered(i, X);
Count += ThisCount;
}
return Count;
That is, if we ran an integer in a loop from 0 to 64K-1... how many times would 11111 appear within those numbers.
Extra rule: 1111110000000000 doesn't count, because it has 6 true values in a row, not 5. So:
1111110000000000 = 0x // because its 6 contiguous true bits, not 5.
1111100000000000 = 1x
0111110000000000 = 1x
0011111000000000 = 1x
1111101111100000 = 2x
I'm trying to do some work involving physically-based random-number generation, and detecting "how random" the numbers are. Thats what this is for.
...
This would be easy to solve if N were less than 32 or so, I could just "run a loop" from 0 to 4GB, then count how many contiguous bits were detected once the loop was completed. Then I could store the number and use it later.
Considering that X ranges from 2 to 16, I'd literally only need to store 15 numbers, each less than 32 bits! (if N=32)!
BUT in my case N = 65,536. So I'd need to run a loop, for 2^65,536 iterations. Basically impossible :)
No way to "experimentally calculate the values for a given X, if N = 65,536". So I need maths, basically.
Fix X and N, obiously with X < N. You have 2^N possible values of combinations of 0 and 1 in your bit number, and you have N-X +1 possible sequences of 1*X (in this part I'm only looking for 1's together) contained in you bit number. Consider for example N = 5 and X = 2, this is a possible valid bit number 01011, so fixed the last two characteres (the last two 1's) you have 2^2 possible combinations for that 1*Xsequence. Then you have two cases:
Border case: Your 1*X is in the border, then you have (2^(N -X -1))*2 possible combinations
Inner case: You have (2^(N -X -2))*(N-X-1) possible combinations.
So, the probability is (border + inner )/2^N
Examples:
1)N = 3, X =2, then the proability is 2/2^3
2) N = 4, X = 2, then the probaility is 5/16
A bit brute force, but I'd do something like this to avoid getting mired in statistics theory:
Multiply the probabilities (1 bit = 0.5, 2 bits = 0.5*0.5, etc) while looping
Keep track of each X and when you have the product of X bits, flip it and continue
Start with small example (N = 5, X=1 - 5) to make sure you get edge cases right, compare to brute force approach.
This can probably be expressed as something like Sum (Sum 0.5^x (x = 1 -> 16) (for n = 1 - 65536) , but edge cases need to be taken into account (i.e. 7 bits doesn't fit, discard probability), which gives me a bit of a headache. :-)
#Andrex answer is plain wrong as it counts some combinations several times.
For example consider the case N=3, X=1. Then the combination 101 happens only 1/2^3 times but the border calculation counts it two times: one as the sequence starting with 10 and another time as the sequence ending with 01.
His calculations gives a (1+4)/8 probability whereas there are only 4 unique sequences that have at least a single contiguous 1 (as opposed to cases such as 011):
001
010
100
101
and so the probability is 4/8.
To count the number of unique sequences you need to account for sequences that can appear multiple times. As long as X is smaller than N/2 this will happens. Not sure how you can count them tho.
N is random number,
I am confused with bound.
Any help is appreciated.
Well, for a random number n, there exists a, b such that 2^a <= n <= 2^b or just a k such that 2^(k-1) <= n <= 2^k - 1 (1). We know that for any number less than 2^n, we need log(2^n) = n * log(2) = n bits to represent it (2). For example:
5: 4 < 5 < 8; we need 3 bits for 4, 5 bits for 8 => we need 4 bits for 5
23: 16 = 2^4 < 23 < 32 = 2^5; so we need 5 bits to represent 23
In conclusion, for an exact number of bits b for a random number n, we can use the formula:
b = floor(log(n)) + 1
So, the big-O notation that we are gonna use is O(floor(log(n)) + 1) = O(logn).
Extra info:
SO Answer
Article
1) I supposed it is a random, integer, positive number (although it's easy to generalize for negative numbers too) which I suppose it is your problem case; for fractional numbers is's a bit harder to generalize this formulae
2) The log notation refers to logarithm in base 2
I'm looking to check if a vector of data is in rank form. That is; each observations is a number between 1 and N, where N is the number of observations, but in a random, unknown order.
The simplest check I could think of was the gaussian sum, using (N * (N + 1)) / 2 and comparing that to the sum of the vector. Except in my case I have 200,000 observations and the sum of all numbers from 1 to 200,000 is greater than 2^32. Apart from getting a 64 bit computer what is the fastest method of checking that data is in rank form.
checkForRanks <- setdiff(1:N,X)
isRanked <- length(checkForRanks) > 0
if(!isRanked) stop("Please rank data")
I was wondering if there is any formula or way to find out much maximum bits will be required if two n bits binary number are multiplied.
I searched a lot for this but was unable to find its answer anywhere.
Number of digits in base B required for representing a number N is floor(log_B(N)+1). Logarithm has this nice property that log(X*Y)=log(X)+log(Y), which hints that the number of digits for X*Y is roughly the sum of the number of digits representing X and Y.
It can be simply concluded using examples:
11*11(since 11 is the maximum 2 bit number)=1001(4 bit)
111*111=110001(6 bit)
1111*1111=11100001(8 bit)
11111*11111=1111000001(10 bit)
and so from above we can see your answer is 2*n
The easiest way to think about this is to consider the maximum of the product, which is attained when we use the maximum of the two multiplicands.
If value x is an n-bit number, it is at most 2^n - 1. Think about this, that 2^n requires a one followed by n zeroes.
Thus the largest possible product of two n-bit numbers will be:
(2^n - 1)^2 = 2^(2n) - 2^(n+1) + 1
Now n=1 is something of a special case, since 1*1 = 1 is again a one-bit number. But in general we see that the maximum product is a 2n-bit number, whenever n > 1. E.g. if n=3, the maximum multiplicand is x=7 and the square 49 is a six-bit number.
It's worth noting that the base of the positional system doesn't matter. Whatever formula you come up with for the decimal multiplication will work for the binary multiplication.
Let's apply a bit of deduction and multiply two numbers that have relatively prime numbers of digits: 2 and 3 digits, respectively.
Smallest possible numbers:
10 * 100 = 1000 has 4 digits
Largest possible numbers:
99 * 999 = 98901 has 5 digits
So, for a multiplication of n-digit by m-digit number, we deduce that the upper and lower limits are n+m and n+m-1 digits, respectively. Let's make sure it holds for binary as well:
10 * 100 = 1000 has 4 digits
11 * 111 = 10101 has 5 digits
So, it does hold for binary, and we can expect it to hold for any base.
x has n binary digits means that 2^(n-1) <= x < 2^n, also assume that y has m binary digits. That means:
2^(m+n-2)<=x*y<2^(m+n)
So x*y can have m+n-1 or m+n digits. It easy to construct examples where both cases are possible:
m+n-1: 2*2 has 3 binary digits (m = n = 2)
m+n: 7*7=49=(binary)110001 has 6 binary digits and m = n = 3
I lack the math skills to make this function.
basically, i want to return 2 random prime numbers that when multiplied, yield a number of bits X given as argument.
for example:
if I say my X is 3 then a possible solution would be:
p = 2 and q = 3 becouse 2 * 3 = 6 (110 has 3 bits).
A problem with this statement is that it starts by asking for two "random" prime numbers. Without any explicit statement of the distribution of the required random primes, we are already stuck. (This is the beginning of a classic paradox, where we are asked to generate a "random" integer.)
But suppose that we change the statement to finding any two arbitrary primes, that yield the desired product with a given number of bits x. The answer is trivial.
The set of numbers that have exactly x bits in their binary representation is the half open set of integers [2^(x-1),2^x-1].
Choose an arbitrary prime number that is less than or equal to (2^x-1)/2. Call it p1.
Next, choose a second prime number that lies in the interval (2^(x-1)/p1,(2^x-1)/p1). Call it p2.
It must be true that p1*p2 will be in the desired interval.
For example, given x = 10, so the product must lie in the interval [512,1023], the set of integers with exactly 10 bits. (Note, there are apparently 147 such numbers in that interval, with exactly two prime factors.)
Step 1:
Choose p1 as any prime no larger than 1023/2 = 511.5. I'll pick p1 = 137. Then the second prime factor must be a prime that lies in the interval
[512 1023]/137
ans =
3.7372 7.4672
thus either 5 or 7.
dec2bin(137*[5 7])
ans =
1010101101
1110111111
If you know the number of bits, you can generate a number 2^(x-2) < x < 2^(x-1). Then take the square root and find the closest primes on either side of it. Multiplying them together will, in most cases, get you a number in the correct range. If it's too high, you can take the two primes directly on the lower side of it.
pseudocode:
x = bits
primelist[] = makeprimelist()
rand = randnum between 2^(x-2) and 2^(x-1)
n = findposition(primelist, rand)
do
result = primelist[n]*primelist[n+1]
n--
while result > 2^(x-1)
Note that numbers generated this way will allways have '1' as the highest significant bit, so would be possible to generate a number of x-1 bits and just tack the 1 onto the end.