I am trying to understand the first testcase of this challenge in codeforces.
The description is:
Sergey is testing a next-generation processor. Instead of bytes the processor works with memory cells consisting of n bits. These bits are numbered from 1 to n. An integer is stored in the cell in the following way: the least significant bit is stored in the first bit of the cell, the next significant bit is stored in the second bit, and so on; the most significant bit is stored in the n-th bit.
Now Sergey wants to test the following instruction: "add 1 to the value of the cell". As a result of the instruction, the integer that is written in the cell must be increased by one; if some of the most significant bits of the resulting number do not fit into the cell, they must be discarded.
Sergey wrote certain values of the bits in the cell and is going to add one to its value. How many bits of the cell will change after the operation?
Summary
Given a binary number, add 1 to its decimal value, count how many bits change after the operation?
Testcases
4
1100
= 3
4
1111
= 4
Note
In the first sample the cell ends up with value 0010, in the second sample — with 0000.
In the 2 test case 1111 is 15, so 15 + 1 = 16 (10000 in binary), so all the 1's change, therefore is 4
But in the 2 test case 1100 is 12, so 12 + 1 = 13 (01101), here just the left 1 at the end changes, but the result is 3 why?
You've missed the crucial part: the least significant bit is the first one (i.e. the leftmost one), not the last one, as we usually write binary.
Thus, 1100 is not 12 but 3. And so, 1100 + 1 = 3 + 1 = 4 = 0010, so 3 bits are changed.
The "least significant bit" means literally a bit that is not the most significant, so you can understand it as "the one representing the smallest value". In binary, the bit representing 2^0 is the least significant. So the binary code in your task is written as follows:
bit no. 0 1 2 3 4 (...)
value 2^0 2^1 2^2 2^3 2^4 (...)
| least | most
| significant | significant
| bit | bit
that's why 1100 is:
1100 = 1 * 2^0 + 1 * 2^1 + 0*2^2 + 0*2^3 = 1 + 2 + 0 + 0 = 3
not the other way around (as we write usually).
How do you find the largest power of 2 a number is divisible by using logic function
for example 144 is divisible by 16 which is 2^4.
How would one do this.
I know 144 in binary is 1001 0000 and I have to use a bitwise function.
But what would I use (and or andn orn ?) or perhaps something else and what can I use as my mask?
I know you have to look at the right most number to tell if it is divisible by 2.
Any help is appreciated
I would go with n & -n or with n & (~n + 1), in case you are worried about running across one's complement arithmetic, given the latter works with both arithmetics.
E.g.,
> 144 & (~144 + 1)
< 16
Now a short explanation.
The bitwise NOT (i.e., ~ operator) of a number n gives -(n + 1). It inverts all the bits of n. The number 2 is represented by 00000010 while its negation is 11111101 which equals to -3 (i.e., , see the two's complement representation of signed numbers).
Do not to confuse it with logical negation.
E.g., ~144 = -(144 + 1) = -145.
The bitwise AND (i.e., & operator) compares two bits of the inputs and generates a result of 1 if both are 1, otherwise it returns 0.
Now the main topic.
This is an old tricks that gives the highest power of 2 that n is divisible by. This means that it returns a number with a single one bit, specifically the bottom bit that was set in n.
For example the binary representation of 144 is 010010000. Its bottom 1 bit is the bit in fourth position (counting backward from right and starting at position 0). Thus the higher power of 2 that divides 144 is 16 (i.e., 00010000).
144 & (~144 + 1) = 144 & -144 = 16
16 & ( ~16 + 1) = 16 & - 16 = 16
10 & ( ~10 + 1) = 10 & - 10 = 2
12 & ( ~12 + 1) = 12 & - 12 = 4
11 & ( ~11 + 1) = 11 & - 11 = 1
3 & ( ~ 3 + 1) = 3 & - 3 = 1
Note that if n is not divisible by any power of 2 it returns 1.
Why it works?
The negative of n is produced by inverting its bits via ~, then adding 1 (again, see two's complement definition). This sum causes every 1 bit (starting from the bottom) to overflow until a 0 bit is encountered (let us call it the bit x). Here the overflow process stops, leaving remaining bits (those beyond the current x bit) unchanged. Thus performing & between n and its inverse will result in a binary string containing only the x bit.
An example follows.
010010000 | +144 ~
----------|-------
101101111 | -145 +
1 |
----------|-------
101110000 | -144
101110000 | -144 &
010010000 | +144
----------|-------
000010000 | 16
As part of an algorithm I'm writing, I need to find a way to convert a 10-bit word into a unique 8-bit word. The 10-bit word is made up of 5 pairs, where each pair can only ever equal 0, 1 or 2 (never 3). For example:
|00|10|00|01|10|
This value needs to somehow be consolidated into a single, unique byte.
As each pair can never equal 3, there are a wide range of values that this 10-bit word will never represent, which makes me think that it is possible to create an algorithm to perform this conversion. The simplest way to do this would be to use a lookup table, but it seems like a waste of resources to store ~680 values which will only be used once in my program. I've already tried to incorporate one of the pairs into the others somehow, but every attempt I've made has resulted in a non-unique value, and I'm now very quickly running out of ideas!
Any help?
The number you have is essentially base 3. You just need to convert this to base 2.
There are 5 pairs, so 3^5 = 243 numbers. And 8 bits is 2^8 = 256 numbers, so it's possible.
The simplest way to convert between bases is to go to base 10 first.
So, for your example:
00|10|00|01|10
Base 3: 02012
Base 10: 2*3^3 + 1*3^1 + 2*3^0
= 54 + 3 + 2
= 59
Base 2:
59 % 2 = 1
/2 29 % 2 = 1
/2 14 % 2 = 0
/2 7 % 2 = 1
/2 3 % 2 = 1
/2 1 % 2 = 1
So 111011 is your number in binary
This explains the above process in a bit more detail.
Note that once you have 59 above stored in a 1-byte integer, you'll probably already have what you want, thus explicitly converting to base 2 might not be necessary.
What you basically have is a base 3 number and you want to convert this to a single number 0 - 255, luckily 5 digits in ternary (base 3) gives 243 combinations.
What you'll need to do is:
Digit Action
( 1st x 3^4)
+ (2nd x 3^3)
+ (3rd x 3^2)
+ (4th x 3)
+ (5th)
This will give you a number 0 to 242.
You are considering to store some information in a byte. A byte can contain at most 2 ^ 8 = 256 status.
Your status is totally 3 ^ 5 = 243 < 256. That make the transfer possible.
Consider your pairs are ABCDE (each character can be 0, 1 or 2)
You can just calculate A*3^4 + B*3^3 + C*3^2 + D*3 + E as your result. I guarantee the result will be in range 0 -- 255.
given a sorted array of distinct integers, what is the minimum number of steps required to make the integers contiguous? Here the condition is that: in a step , only one element can be changed and can be either increased or decreased by 1 . For example, if we have 2,4,5,6 then '2' can be made '3' thus making the elements contiguous(3,4,5,6) .Hence the minimum steps here is 1 . Similarly for the array: 2,4,5,8:
Step 1: '2' can be made '3'
Step 2: '8' can be made '7'
Step 3: '7' can be made '6'
Thus the sequence now is 3,4,5,6 and the number of steps is 3.
I tried as follows but am not sure if its correct?
//n is the number of elements in array a
int count=a[n-1]-a[0]-1;
for(i=1;i<=n-2;i++)
{
count--;
}
printf("%d\n",count);
Thanks.
The intuitive guess is that the "center" of the optimal sequence will be the arithmetic average, but this is not the case. Let's find the correct solution with some vector math:
Part 1: Assuming the first number is to be left alone (we'll deal with this assumption later), calculate the differences, so 1 12 3 14 5 16-1 2 3 4 5 6 would yield 0 -10 0 -10 0 -10.
sidenote: Notice that a "contiguous" array by your implied definition would be an increasing arithmetic sequence with difference 1. (Note that there are other reasonable interpretations of your question: some people may consider 5 4 3 2 1 to be contiguous, or 5 3 1 to be contiguous, or 1 2 3 2 3 to be contiguous. You also did not specify if negative numbers should be treated any differently.)
theorem: The contiguous numbers must lie between the minimum and maximum number. [proof left to reader]
Part 2: Now returning to our example, assuming we took the 30 steps (sum(abs(0 -10 0 -10 0 -10))=30) required to turn 1 12 3 14 5 16 into 1 2 3 4 5 6. This is one correct answer. But 0 -10 0 -10 0 -10+c is also an answer which yields an arithmetic sequence of difference 1, for any constant c. In order to minimize the number of "steps", we must pick an appropriate c. In this case, each time we increase or decrease c, we increase the number of steps by N=6 (the length of the vector). So for example if we wanted to turn our original sequence 1 12 3 14 5 16 into 3 4 5 6 7 8 (c=2), then the differences would have been 2 -8 2 -8 2 -8, and sum(abs(2 -8 2 -8 2 -8))=30.
Now this is very clear if you could picture it visually, but it's sort of hard to type out in text. First we took our difference vector. Imagine you drew it like so:
4|
3| *
2| * |
1| | | *
0+--+--+--+--+--*
-1| |
-2| *
We are free to "shift" this vector up and down by adding or subtracting 1 from everything. (This is equivalent to finding c.) We wish to find the shift which minimizes the number of | you see (the area between the curve and the x-axis). This is NOT the average (that would be minimizing the standard deviation or RMS error, not the absolute error). To find the minimizing c, let's think of this as a function and consider its derivative. If the differences are all far away from the x-axis (we're trying to make 101 112 103 114 105 116), it makes sense to just not add this extra stuff, so we shift the function down towards the x-axis. Each time we decrease c, we improve the solution by 6. Now suppose that one of the *s passes the x axis. Each time we decrease c, we improve the solution by 5-1=4 (we save 5 steps of work, but have to do 1 extra step of work for the * below the x-axis). Eventually when HALF the *s are past the x-axis, we can NO LONGER IMPROVE THE SOLUTION (derivative: 3-3=0). (In fact soon we begin to make the solution worse, and can never make it better again. Not only have we found the minimum of this function, but we can see it is a global minimum.)
Thus the solution is as follows: Pretend the first number is in place. Calculate the vector of differences. Minimize the sum of the absolute value of this vector; do this by finding the median OF THE DIFFERENCES and subtracting that off from the differences to obtain an improved differences-vector. The sum of the absolute value of the "improved" vector is your answer. This is O(N) The solutions of equal optimality will (as per the above) always be "adjacent". A unique solution exists only if there are an odd number of numbers; otherwise if there are an even number of numbers, AND the median-of-differences is not an integer, the equally-optimal solutions will have difference-vectors with corrective factors of any number between the two medians.
So I guess this wouldn't be complete without a final example.
input: 2 3 4 10 14 14 15 100
difference vector: 2 3 4 5 6 7 8 9-2 3 4 10 14 14 15 100 = 0 0 0 -5 -8 -7 -7 -91
note that the medians of the difference-vector are not in the middle anymore, we need to perform an O(N) median-finding algorithm to extract them...
medians of difference-vector are -5 and -7
let us take -5 to be our correction factor (any number between the medians, such as -6 or -7, would also be a valid choice)
thus our new goal is 2 3 4 5 6 7 8 9+5=7 8 9 10 11 12 13 14, and the new differences are 5 5 5 0 -3 -2 -2 -86*
this means we will need to do 5+5+5+0+3+2+2+86=108 steps
*(we obtain this by repeating step 2 with our new target, or by adding 5 to each number of the previous difference... but since you only care about the sum, we'd just add 8*5 (vector length times correct factor) to the previously calculated sum)
Alternatively, we could have also taken -6 or -7 to be our correction factor. Let's say we took -7...
then the new goal would have been 2 3 4 5 6 7 8 9+7=9 10 11 12 13 14 15 16, and the new differences would have been 7 7 7 2 1 0 0 -84
this would have meant we'd need to do 7+7+7+2+1+0+0+84=108 steps, the same as above
If you simulate this yourself, can see the number of steps becomes >108 as we take offsets further away from the range [-5,-7].
Pseudocode:
def minSteps(array A of size N):
A' = [0,1,...,N-1]
diffs = A'-A
medianOfDiffs = leftMedian(diffs)
return sum(abs(diffs-medianOfDiffs))
Python:
leftMedian = lambda x:sorted(x)[len(x)//2]
def minSteps(array):
target = range(len(array))
diffs = [t-a for t,a in zip(target,array)]
medianOfDiffs = leftMedian(diffs)
return sum(abs(d-medianOfDiffs) for d in diffs)
edit:
It turns out that for arrays of distinct integers, this is equivalent to a simpler solution: picking one of the (up to 2) medians, assuming it doesn't move, and moving other numbers accordingly. This simpler method often gives incorrect answers if you have any duplicates, but the OP didn't ask that, so that would be a simpler and more elegant solution. Additionally we can use the proof I've given in this solution to justify the "assume the median doesn't move" solution as follows: the corrective factor will always be in the center of the array (i.e. the median of the differences will be from the median of the numbers). Thus any restriction which also guarantees this can be used to create variations of this brainteaser.
Get one of the medians of all the numbers. As the numbers are already sorted, this shouldn't be a big deal. Assume that median does not move. Then compute the total cost of moving all the numbers accordingly. This should give the answer.
community edit:
def minSteps(a):
"""INPUT: list of sorted unique integers"""
oneMedian = a[floor(n/2)]
aTarget = [oneMedian + (i-floor(n/2)) for i in range(len(a))]
# aTargets looks roughly like [m-n/2?, ..., m-1, m, m+1, ..., m+n/2]
return sum(abs(aTarget[i]-a[i]) for i in range(len(a)))
This is probably not an ideal solution, but a first idea.
Given a sorted sequence [x1, x2, …, xn]:
Write a function that returns the differences of an element to the previous and to the next element, i.e. (xn – xn–1, xn+1 – xn).
If the difference to the previous element is > 1, you would have to increase all previous elements by xn – xn–1 – 1. That is, the number of necessary steps would increase by the number of previous elements × (xn – xn–1 – 1). Let's call this number a.
If the difference to the next element is >1, you would have to decrease all subsequent elements by xn+1 – xn – 1. That is, the number of necessary steps would increase by the number of subsequent elements × (xn+1 – xn – 1). Let's call this number b.
If a < b, then increase all previous elements until they are contiguous to the current element. If a > b, then decrease all subsequent elements until they are contiguous to the current element. If a = b, it doesn't matter which of these two actions is chosen.
Add up the number of steps taken in the previous step (by increasing the total number of necessary steps by either a or b), and repeat until all elements are contiguous.
First of all, imagine that we pick an arbitrary target of contiguous increasing values and then calculate the cost (number of steps required) for modifying the array the array to match.
Original: 3 5 7 8 10 16
Target: 4 5 6 7 8 9
Difference: +1 0 -1 -1 -2 -7 -> Cost = 12
Sign: + 0 - - - -
Because the input array is already ordered and distinct, it is strictly increasing. Because of this, it can be shown that the differences will always be non-increasing.
If we change the target by increasing it by 1, the cost will change. Each position in which the difference is currently positive or zero will incur an increase in cost by 1. Each position in which the difference is currently negative will yield a decrease in cost by 1:
Original: 3 5 7 8 10 16
New target: 5 6 7 8 9 10
New Difference: +2 +1 0 0 -1 -6 -> Cost = 10 (decrease by 2)
Conversely, if we decrease the target by 1, each position in which the difference is currently positive will yield a decrease in cost by 1, while each position in which the difference is zero or negative will incur an increase in cost by 1:
Original: 3 5 7 8 10 16
New target: 3 4 5 6 7 8
New Difference: 0 -1 -2 -2 -3 -8 -> Cost = 16 (increase by 4)
In order to find the optimal values for the target array, we must find a target such that any change (increment or decrement) will not decrease the cost. Note that an increment of the target can only decrease the cost when there are more positions with negative difference than there are with zero or positive difference. A decrement can only decrease the cost when there are more positions with a positive difference than with a zero or negative difference.
Here are some example distributions of difference signs. Remember that the differences array is non-increasing, so positives always have to be first and negatives last:
C C
+ + + - - - optimal
+ + 0 - - - optimal
0 0 0 - - - optimal
+ 0 - - - - can increment (negatives exceed positives & zeroes)
+ + + 0 0 0 optimal
+ + + + - - can decrement (positives exceed negatives & zeroes)
+ + 0 0 - - optimal
+ 0 0 0 0 0 optimal
C C
Observe that if one of the central elements (marked C) is zero, the target must be optimal. In such a circumstance, at best any increment or decrement will not change the cost, but it may increase it. This result is important, because it gives us a trivial solution. We pick a target such that a[n/2] remains unchanged. There may be other possible targets that yield the same cost, but there are definitely none that are better. Here's the original code modified to calculate this cost:
//n is the number of elements in array a
int targetValue;
int cost = 0;
int middle = n / 2;
int startValue = a[middle] - middle;
for (i = 0; i < n; i++)
{
targetValue = startValue + i;
cost += abs(targetValue - a[i]);
}
printf("%d\n",cost);
You can not do it by iterating once on the array, that's for sure.
You need first to check the difference between each two numbers, for example:
2,7,8,9 can be 2,3,4,5 with 18 steps or 6,7,8,9 with 4 steps.
Create a new array with the difference like so: for 2,7,8,9 it wiil be 4,1,1. Now you can decide whether to increase or decrease the first number.
Lets assume that the contiguous array looks something like this -
c c+1 c+2 c+3 .. and so on
Now lets take an example -
5 7 8 10
The contiguous array in this case will be -
c c+1 c+2 c+3
In order to get the minimum steps, the sum of the modulus of the difference of the integers(before and after) w.r.t the ith index should be the minimum. In which case,
(c-5)^2 + (c-6)^2 + (c-6)^2 + (c-7)^2 should be minimum
Let f(c) = (c-5)^2 + (c-6)^2 + (c-6)^2 + (c-7)^2
= 4c^2 - 48c + 146
Applying differential calculus to get the minima,
f'(c) = 8c - 48 = 0
=> c = 6
So our contiguous array is 6 7 8 9 and the minimum cost here is 2.
To sum it up, just generate f(c), get the first differential and find out c.
This should take O(n).
Brute force approach O(N*M)
If one draws a line through each point in the array a then y0 is a value where each line starts at index 0. Then the answer is the minimum among number of steps reqired to get from a to every line that starts at y0, in Python:
y0s = set((y - i) for i, y in enumerate(a))
nsteps = min(sum(abs(y-(y0+i)) for i, y in enumerate(a))
for y0 in xrange(min(y0s), max(y0s)+1)))
Input
2,4,5,6
2,4,5,8
Output
1
3
I am reading a algorithms book by S.DasGupta. Following is text snippet from the text regarding number of bits required for nth Fibonacci number.
It is reasonable to treat addition as
a single computer step if small
numbers are being added, 32-bit
numbers say. But the nth Fibonacci
number is about
0.694n bits long, and this can far exceed 32 as n grows. Arithmetic
operations on arbitrarily large
numbers cannot possibly be performed
in a single, constant-time step.
My question is for eg, for Fibonacci number F1 = 1, F2 =1, F3=2, and so on. then substituting "n" in above formula i.e., 0.694n for F1 is approximately 1, F2 is approximately 2 bits, but for F3 and so on above formula fails. I think i didn't understand propely what author mean here, can any one please help me in understanding this?
Thanks
Well,
n 3 4 5 6 7 8
0.694n 2.08 2.78 3.47 4.16 4.86 5.55
F(n) 2 3 5 8 13 21
bits 2 2 3 4 4 5
log(F(n)) 1 1.58 2.32 3 3.7 4.39
Bits required is the base-2 log rounded up, so this is close enough for me.
The value 0.694 comes from the fact that F(n) is the closest integer to (φn)/√5. So log(F(n)) is n * log(phi) - log(sqrt(5)), and log(phi) is 0.694. As n gets bigger, the log(sqrt(5)) and the rounding rapidly become insignificant.
private static int nobFib(int n) // number of bits Fib(n)
{
return n < 6 ? ++n/2 : (int)(0.69424191363061738 * n - 0.1609640474436813);
}
Checked it for n from 0 to 500.000, n=500.000.000, n=1.000.000.000
It's based on Binet's formula.
Needed it for: Fibonacci Sequence Binary Plot.
See: http://bigintegers.blogspot.com/2012/09/fibonacci-sequence-binary-plot-edd-peg.html
First of all, the word about is very important, as in the nth Fibonacci number is about 0.694n bits long. Second, I think the author means when n->infinity. Try some big number and check :)
you cant have say half a bit... the amount of bits must be rounded
so it means
number of bits = Math.ceil(Math.max(0.694*n,32));
so its rounded up for n>32 and 32 for n<32
for 32bit systems that is
and the number may not be exact
I think he's just using the Fibonacci numbers to illustrate his point that for large numbers (>32 bit) addition cannot be assumed to be constant anymore because it involves more than a singe instruction on the CPU.
Why does the formula fail? For F3=2 the binary representation needs 2bits (3 * 0.694 = 2.082) Take F50=12586269025, which can be represented using 33bits (50 * 0.694 = 35) which is still reasonably close to the true value.
N F(N) 0.694*N
1 0 1
2 1 1
3 1 1
4 2 2
5 3 2
6 5 3
7 8 4
8 13 4
etc. That's my interpretation. But then, that means that you have to get to f(47) = 1,836,311,903 before you exceed 32 bits.
The author is basically describing how large numbers affect the performance of the algorithm. To be overly simple, a processor can add numbers of the register size very quickly, if the numbers exceed the register size, more low level processor instructions need to be executed.