suppose I have n1 and n2 I want to multiply them
for example I have array
n1={1,2,3};
and in
n2={5,6}
they are two integers in n1 we have the 123 and in n2 56
123*56=6888
then in result I should have
result = {6,8,8,8}
here is the incomplete algorithm which I thought
for(i in n1 bigger array)
for(j in n2 smaller one)
{
mult=n1[i]*n2[j]
mult+= carry;
if(mult>=10)
{
carry = (mult/10);
mult-= (carry*10);
}
}
}
How can I write it? I don't know the place of store
after finishing the insider loop I should store num in array and then compute again and...
How should I write it? I searched the whole of overflow here but I didn't find about it in c code
The Goal is to Compute the Large numbers Integer Numbers has 8 Bytes,in other words 64 bits so they can store 2pow64-1 which is 19 digits now this will help to compute very larger than 19 digits
It would be slightly easier if your digit-arrays were little-endian. Then your example multiplication would look
3 2 1 * 6 5
---------------
18 12 6
15 10 5
---------------
18 27 16 5 // now propagate carries
8 28 16 5
8 8 18 5
8 8 8 6
============
The product of n1[i] and n2[j] would contribute to result[i+j]. The main loop could roughly look like
for (i = 0; i < l1; ++i) // l1 is length of n1
{
for (j = 0; j < l2; ++j) // l2 is length of n2
{
result[i+j] += n1[i]*n2[j];
}
}
// now carry propagation
You see that the result must be at least (l1-1) + (l2-1) + 1 long, since the product of the most significant digits goes int result[(l1-1) + (l2-1)]. On the other hand, n1 < 10^l1 and n2 < 10^l2, so the product is < 10^(l1+l2) and you need at most l1+l2 digits.
But if you're working with char (signed or unsigned), that will quickly overflow in each digit, since (for k <= min(l1-1,l2-1)) k+1 products of two digits (each can be as large as 81) contribute to digit k of the product.
So it's better to perform the multiplication grouped according to the result digit, accumulating in a larger type, and doing carry propagation on writing the result digit. With little-endian numbers
char *mult(char *n1, size_t l1, char *n2, size_t l2, size_t *rl)
{
// allocate and zero-initialise, may be one more digit than needed
char *result = calloc(l1+l2+1,1);
*rl = l1 + l2;
size_t k, i, lim = l1+l2-1;
for (k = 0; k < lim; ++k)
{
unsigned long accum = result[k];
for (i = (k < l2) ? 0 : k-(l2-1); i <= k && i < l1; ++i)
{
accum += (n1[i] - '0') * (n2[k-i] - '0');
}
result[k] = accum % 10 + '0';
accum /= 10;
i = k+1;
while(accum > 0)
{
result[i] += accum % 10;
accum /= 10;
++i;
}
}
if (result[l1+l2-1] == 0)
{
*rl -= 1;
char *real_result = calloc(l1+l2,1);
for (i = 0; i < l1+l2-1; ++i)
{
real_result[i] = result[i];
}
free(result);
return real_result;
}
else
{
result[l1+l2-1] += '0';
return result;
}
}
For big-endian numbers, the indexing has to be modified - you can figure that out yourself, hopefully - but the principle remains the same.
Indeed, the result isn't much different after tracking indices with pencil and paper:
char *mult(char *n1, size_t l1, char *n2, size_t l2, size_t *rl)
{
// allocate and zero-initialise, may be one more digit than needed
// we need (l1+l2-1) or (l1+l2) digits for the product and a 0-terminator
char *result = calloc(l1+l2+1,1);
*rl = l1 + l2;
size_t k, i, lim = l1+l2-1;
// calculate the product from least significant digit to
// most significant, least significant goes into result[l1+l2-1],
// the digit result[0] can only be nonzero by carry propagation.
for (k = lim; k > 0; --k)
{
unsigned long accum = result[k]; // start with carry
for (i = (k < l2) ? 0 : k-l2; i < k && i < l1; ++i)
{
accum += (n1[i] - '0') * (n2[k-1-i] - '0');
}
result[k] = accum % 10 + '0';
accum /= 10;
i = k-1;
while(accum > 0)
{
result[i] += accum % 10;
accum /= 10;
--i;
}
}
if (result[0] == 0) // no carry in digit 0, we allocated too much
{
*rl -= 1;
char *real_result = calloc(l1+l2,1);
for (i = 0; i < l1+l2-1; ++i)
{
real_result[i] = result[i+1];
}
free(result);
return real_result;
}
else
{
result[0] += '0'; // make it an ASCII digit
return result;
}
}
Edit: added 0-terminators
Note: these are not NUL-terminated (unsigned) char arrays, so we need to keep length information (that's good to do anyway), hence it would be better to store that info together with the digit array in a struct. Also, as written it only works for positive numbers. Dealing with negative numbers is awkward if you only have raw arrays, so another point for storing additional info.
Keeping the digits as '0' + value doesn't make sense for the computations, it is only convenient for printing, but that only if they were NUL-terminated arrays. You may want to add a slot for the NUL-terminator then. In that case, the parameter rl in which we store the length of the product is not strictly necessary.
Definitely an interesting problem.
Here was my thought:
For the given array, append each value to the end of a string. Thus you construct a string of the numbers in order. {1,2,3} = "123"
Then, you use a "ToInteger" method that you can find in one of the C libraries. Now you have your number to multiply with.
With this logic, you can probably look up how the "ToInteger" or "ToString" methods work with numbers, which would lead to an answer.
Think how you would do it on paper, since you are simulating multiplying two decimal numbers. For starters, I think you'd go from least significant to most significant digit, so you'd be counting down the indexes (2, 1, 0 for the larger array; 1, 0 for the smaller). Also, you'd somehow have to arrange that when you multiply by n2[0] (the 5 in 56), you start adding at the tens place, not the units.
You won't find complete C code for your problem at SO. Your first approach isn't that bad. You could do the following:
Multiply n1 and n2, conversion is done by mulitplication and addition, i. e. a{1,2,3} -> 1*100 + 2*10 + 3*1, easy to implement
Count the digits of your multiplication result (use division inside a loop)
While looping through the digits you can store them back into another array
If you can't or if you don't want to deal with dynamic array allocation, then think about how big your array for storage must be beforehand and perform a static allocation.
Edit
Based on the discussion another approach:
Suppose, that r = n1 * n2
Create a n*m 2D array, where
n = number of digits in n2
m = number of digits in n1 + 1
Within a loop multiply each digit of n1 with one of the elements of n2, store the result in the array, store the result per-digit in the 2D-array, don't forget to add the carry to each digit
Repeat 2 with all other digits of n2
Now the array is filled and you'll have to add each digits like you would do it on paper, store each result within a target array, take care of the carry again
There is one thing left in the algorithm: Determine the size of the target array, based on the informations within the intermediate array, you can think about this by using pencil and paper ;)
This code isn't optimized, nor does it account for generic lengths of arrays/numbers, but it should give you the general idea of how to implement the algorithm:
(This is similar to string-to-int or int-to-string algorithms, just add the ASCII offset to each item of the array and you have it.)
#include <stdio.h>
#include <stdint.h>
#define N1_N 3
#define N2_N 2
#define MAX_N 4 /* maximum array length allowed */
void print_array (const uint8_t* array, size_t size);
uint32_t array_to_ulong (const uint8_t* array, size_t size);
size_t ulong_to_array (uint8_t* array, size_t size, uint32_t val);
int main()
{
uint8_t n1[N1_N] = {1,2,3};
uint8_t n2[N2_N] = {5,6};
uint8_t n3[MAX_N];
size_t n3_size = MAX_N;
uint32_t n1_int;
uint32_t n2_int;
uint32_t result;
print_array(n1, N1_N);
printf(" * ");
print_array(n2, N2_N);
n1_int = array_to_ulong (n1, N1_N);
n2_int = array_to_ulong (n2, N2_N);
result = n1_int * n2_int;
printf(" = %d = ", result);
n3_size = ulong_to_array (n3, n3_size, result);
print_array(n3, n3_size);
getchar();
return 0;
}
void print_array (const uint8_t* array, size_t size)
{
size_t i;
printf("{");
for(i=0; i<size; i++)
{
printf("%d", array[i]);
if(i != size-1)
{
printf(", ");
}
}
printf("}");
}
uint32_t array_to_ulong (const uint8_t* array, size_t size)
{
uint32_t result = 0;
uint32_t multiplier = 1;
size_t i;
for(i=1; i<=size; i++)
{
result += array[size-i] * multiplier;
multiplier *= 10;
}
return result;
}
size_t ulong_to_array (uint8_t* array, size_t size, uint32_t val)
{
size_t i;
for(i=1; i<=size && val!=0; i++)
{
array[size-i] = val % 10;
val /= 10;
}
return i-1;
}
12345 * 6789 is:
12345 * 6 * 1000 +
12345 * 7 * 100 +
12345 * 8 * 10 +
12345 * 9 * 1
and that is:
1 * 6*1000 * 10000 + 2 * 6*1000 * 1000 + 3 * 6*1000 * 100 + 4 * 6*1000 * 10 + 5 * 6*1000 * 1 +
1 * 7*100 * 10000 + 2 * 7*100 * 1000 + 3 * 7*100 * 100 + 4 * 7*100 * 10 + 5 * 7*100 * 1 +
1 * 8*10 * 10000 + 2 * 8*10 * 1000 + 3 * 8*10 * 100 + 4 * 8*10 * 10 + 5 * 8*10 * 1 +
1 * 9*1 * 10000 + 2 * 9*1 * 1000 + 3 * 9*1 * 100 + 4 * 9*1 * 10 + 5 * 9*1 * 1
so the algorith is multiply each value by each value and add (cumulate) it to the appropriate result array element (1000 is 10^3 so array element 3 (array starting by zero)).
then move thru the result array and shift for results bigger than 10 the div by ten to the left (starting by the far right)
#include<stdio.h>
#include<math.h>
#include<stdlib.h>
#include<string.h>
#define MAX 10000
char * multiply(char [],char[]);
int main(){
char a[MAX];
char b[MAX];
char *c;
int la,lb;
int i;
printf("Enter the first number : ");
scanf("%s",a);
printf("Enter the second number : ");
scanf("%s",b);
printf("Multiplication of two numbers : ");
c = multiply(a,b);
printf("%s",c);
return 0;
}
char * multiply(char a[],char b[]){
static char mul[MAX];
char c[MAX];
char temp[MAX];
int la,lb;
int i,j,k=0,x=0,y;
long int r=0;
long sum = 0;
la=strlen(a)-1;
lb=strlen(b)-1;
for(i=0;i<=la;i++){
a[i] = a[i] - 48;
}
for(i=0;i<=lb;i++){
b[i] = b[i] - 48;
}
for(i=lb;i>=0;i--){
r=0;
for(j=la;j>=0;j--){
temp[k++] = (b[i]*a[j] + r)%10;
r = (b[i]*a[j]+r)/10;
}
temp[k++] = r;
x++;
for(y = 0;y<x;y++){
temp[k++] = 0;
}
}
k=0;
r=0;
for(i=0;i<la+lb+2;i++){
sum =0;
y=0;
for(j=1;j<=lb+1;j++){
if(i <= la+j){
sum = sum + temp[y+i];
}
y += j + la + 1;
}
c[k++] = (sum+r) %10;
r = (sum+r)/10;
}
c[k] = r;
j=0;
for(i=k-1;i>=0;i--){
mul[j++]=c[i] + 48;
}
mul[j]='\0';
return mul;
}
Related
I need to write a program that takes 2 digits(X and n) and then prints X with last n digits of X reversed.
For example
Input: 12345 3
Output: 12543
Input: 523 2
Output: 532
I already wrote a control mechanism for checking n is greater or equal than the number of digits of X
For example if inputs are 6343 and 7, program prints that inputs should be changed and takes input again.
My main problem is I couldn't find an algorithm for reversing last n digits. I can reverse any int with this code
int X, r = 0;
printf("Enter a number to reverse\n");
scanf("%d", &n);
while (X != 0)
{
r = r * 10;
r = r + n%10;
X = X/10;
}
printf("Reverse of the number = %d", r);
But I couldn't figure how two reverse just last digits. Can you give me any idea for that?
I couldn't figure how to reverse just last digits
Separate the number using pow(10,n) - see later code.
unsigned reverse_last_digits(unsigned x, unsigned n) {
unsigned pow10 = powu(10, n);
unsigned lower = x%pow10;
unsigned upper = x - lower;
return upper + reverseu(lower, n);
}
Create a loop that extracts the least-significant-digit (%10) and builds up another integer by applying that digit. (y = y*10 + new_digit)
unsigned reverseu(unsigned x, unsigned n) {
unsigned y = 0;
while (n-- > 0) {
y = y*10 + x%10;
x /= 10;
}
return y;
}
For integer type problems, consider integer helper functions and avoid floating point functions like pow() as they may provide only an approximate results. Easy enough to code an integer pow().
unsigned powu(unsigned x, unsigned expo) {
unsigned y = 1;
while (expo > 0) {
if (expo & 1) {
y = x * y;
}
expo >>= 1;
x *= x;
}
return y;
}
Test
int main() {
printf("%u\n", reverse_last_digits(12345, 3));
printf("%u\n", reverse_last_digits(523, 2));
printf("%u\n", reverse_last_digits(42001, 3));
printf("%u\n", reverse_last_digits(1, 2));
}
Output
12543
532
42100
10
Code uses unsigned rather than int to avoid undefined behavior (UB) on int overflow.
It is an easy one.
1. let say the number you want to reverse is curr_number;
2. Now, the places you want to reverse is x;
(remember to verify that x must be less than the number of digit of curr_number);
3. now, just take a temp integer and store curr_number / pow(10,x) ('/' = divide and pow(10,x) is 10 to the power x)
4. now, take a second number temp2, which will store curr_number-(temp * pow(10,x) )
5. reverse this temp2 (using your function)
6. now, answer = (temp * pow(10,x) ) + (temp2) //(note temp2 is reversed)
example with steps:
curr_number = 1234567
places you want to reverse is 3
temp = 1234567 / (10^3) i.e (1234567/1000) = 1234 (because it is int type)
temp2 = 1234567 - (1234*10^3) i.e 1234567 - 1234000 = 567
reverse(567) = 765
answer = (1234 * 10^3) + 765 = 1234765
Create two variables
lastn which stores the last n digits (345)
r which stores the reversed last n digits (543)
Subtract lastn from the original number (12345 - 345 = 12000)
Add r to the above number (12000 + 543 = 12543)
int c = 0; // count number of digits
int original = x;
int lastn = 0;
while (x != 0 && c < n) {
r = r * 10;
r = r + x % 10;
lastn += (x % 10) * pow(10, c);
x = x / 10;
c++;
}
printf("reversed: %d\n", original - lastn + r);
In case you don't have problems using char, you can do this
#include <stdio.h>
#include <string.h>
#define SIZE 10
int main() {
char n[SIZE]; // the Number;
int x; // number of last digits of n to reverse
int len; // number of digits of n
scanf("%s%d", n, &x);
len = strlen(n);
for(int i = 0; i < len; i++) {
i < len - x ? printf("%c", n[i]) : printf("%c", n[2*len -1 - i - x]);
}
return 0;
}
If you want you can make the program more readable by splitting the for in two
for(int i = 0; i < len - x; i++) {
printf("%c", n[i]);
}
for(int i = len-1; i >= len - x; i--) {
printf("%c", n[i]);
}
Note: the program won't work if n > x (i.e. if you want to swap more digits than you got)
Let's say I've been given two integers a, b where a is a positive integer and is smaller than b. I have to find an efficient algorithm that's going to give me the sum of number of base2 digits (number of bits) over the interval [a, b]. For example, in the interval [0, 4] the sum of digits is equal to 9 because 0 = 1 digit, 1 = 1 digit, 2 = 2 digits, 3 = 2 digits and 4 = 3 digits.
My program is capable of calculating this number by using a loop but I'm looking for something more efficient for large numbers. Here are the snippets of my code just to give you an idea:
int numberOfBits(int i) {
if(i == 0) {
return 1;
}
else {
return (int) log2(i) + 1;
}
}
The function above is for calculating the number of digits of one number in the interval.
The code below shows you how I use it in my main function.
for(i = a; i <= b; i++) {
l = l + numberOfBits(i);
}
printf("Digits: %d\n", l);
Ideally I should be able to get the number of digits by using the two values of my interval and using some special algorithm to do that.
Try this code, i think it gives you what you are needing to calculate the binaries:
int bit(int x)
{
if(!x) return 1;
else
{
int i;
for(i = 0; x; i++, x >>= 1);
return i;
}
}
The main thing to understand here is that the number of digits used to represent a number in binary increases by one with each power of two:
+--------------+---------------+
| number range | binary digits |
+==============+===============+
| 0 - 1 | 1 |
+--------------+---------------+
| 2 - 3 | 2 |
+--------------+---------------+
| 4 - 7 | 3 |
+--------------+---------------+
| 8 - 15 | 4 |
+--------------+---------------+
| 16 - 31 | 5 |
+--------------+---------------+
| 32 - 63 | 6 |
+--------------+---------------+
| ... | ... |
A trivial improvement over your brute force algorithm would then be to figure out how many times this number of digits has increased between the two numbers passed in (given by the base two logarithm) and add up the digits by multiplying the count of numbers that can be represented by the given number of digits (given by the power of two) with the number of digits.
A naive implementation of this algorithm is:
int digits_sum_seq(int a, int b)
{
int sum = 0;
int i = 0;
int log2b = b <= 0 ? 1 : floor(log2(b));
int log2a = a <= 0 ? 1 : floor(log2(a)) + 1;
sum += (pow(2, log2a) - a) * (log2a);
for (i = log2b; i > log2a; i--)
sum += pow(2, i - 1) * i;
sum += (b - pow(2, log2b) + 1) * (log2b + 1);
return sum;
}
It can then be improved by the more efficient versions of the log and pow functions seen in the other answers.
First, we can improve the speed of log2, but that only gives us a fixed factor speed-up and doesn't change the scaling.
Faster log2 adapted from: https://graphics.stanford.edu/~seander/bithacks.html#IntegerLogLookup
The lookup table method takes only about 7 operations to find the log
of a 32-bit value. If extended for 64-bit quantities, it would take
roughly 9 operations. Another operation can be trimmed off by using
four tables, with the possible additions incorporated into each. Using
int table elements may be faster, depending on your architecture.
Second, we must re-think the algorithm. If you know that numbers between N and M have the same number of digits, would you add them up one by one or would you rather do (M-N+1)*numDigits?
But if we have a range where multiple numbers appear what do we do? Let's just find the intervals of same digits, and add sums of those intervals. Implemented below. I think that my findEndLimit could be further optimized with a lookup table.
Code
#include <stdio.h>
#include <limits.h>
#include <time.h>
unsigned int fastLog2(unsigned int v)
{
static const char LogTable256[256] =
{
#define LT(n) n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n
-1, 0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
LT(4), LT(5), LT(5), LT(6), LT(6), LT(6), LT(6),
LT(7), LT(7), LT(7), LT(7), LT(7), LT(7), LT(7), LT(7)
};
register unsigned int t, tt; // temporaries
if (tt = v >> 16)
{
return (t = tt >> 8) ? 24 + LogTable256[t] : 16 + LogTable256[tt];
}
else
{
return (t = v >> 8) ? 8 + LogTable256[t] : LogTable256[v];
}
}
unsigned int numberOfBits(unsigned int i)
{
if (i == 0) {
return 1;
}
else {
return fastLog2(i) + 1;
}
}
unsigned int findEndLimit(unsigned int sx, unsigned int ex)
{
unsigned int sy = numberOfBits(sx);
unsigned int ey = numberOfBits(ex);
unsigned int mx;
unsigned int my;
if (sy == ey) // this also means sx == ex
return ex;
// assumes sy < ey
mx = (ex - sx) / 2 + sx; // will eq. sx for sx + 1 == ex
my = numberOfBits(mx);
while (ex - sx != 1) {
mx = (ex - sx) / 2 + sx; // will eq. sx for sx + 1 == ex
my = numberOfBits(mx);
if (my == ey) {
ex = mx;
ey = numberOfBits(ex);
}
else {
sx = mx;
sy = numberOfBits(sx);
}
}
return sx+1;
}
int main(void)
{
unsigned int a, b, m;
unsigned long l;
clock_t start, end;
l = 0;
a = 0;
b = UINT_MAX;
start = clock();
unsigned int i;
for (i = a; i < b; ++i) {
l += numberOfBits(i);
}
if (i == b) {
l += numberOfBits(i);
}
end = clock();
printf("Naive\n");
printf("Digits: %ld; Time: %fs\n",l, ((double)(end-start))/CLOCKS_PER_SEC);
l=0;
start = clock();
do {
m = findEndLimit(a, b);
l += (b-m + 1) * (unsigned long)numberOfBits(b);
b = m-1;
} while (b > a);
l += (b-a+1) * (unsigned long)numberOfBits(b);
end = clock();
printf("Binary search\n");
printf("Digits: %ld; Time: %fs\n",l, ((double)(end-start))/CLOCKS_PER_SEC);
}
Output
From 0 to UINT_MAX
$ ./main
Naive
Digits: 133143986178; Time: 25.722492s
Binary search
Digits: 133143986178; Time: 0.000025s
My findEndLimit can take long time in some edge cases:
From UINT_MAX/16+1 to UINT_MAX/8
$ ./main
Naive
Digits: 7784628224; Time: 1.651067s
Binary search
Digits: 7784628224; Time: 4.921520s
Conceptually, you would need to split the task to two subproblems -
1) find the sum of digits from 0..M, and from 0..N, then subtract.
2) find the floor(log2(x)), because eg for the number 77 the numbers 64,65,...77 all have 6 digits, the next 32 have 5 digits, the next 16 have 4 digits and so on, which makes a geometric progression.
Thus:
int digits(int a) {
if (a == 0) return 1; // should digits(0) be 0 or 1 ?
int b=(int)floor(log2(a)); // use any all-integer calculation hack
int sum = 1 + (b+1) * (a- (1<<b) +1); // added 1, due to digits(0)==1
while (--b)
sum += (b + 1) << b; // shortcut for (b + 1) * (1 << b);
return sum;
}
int digits_range(int a, int b) {
if (a <= 0 || b <= 0) return -1; // formulas work for strictly positive numbers
return digits(b)-digits(a-1);
}
As efficiency depends on the tools available, one approach would be doing it "analog":
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
unsigned long long pow2sum_min(unsigned long long n, long long unsigned m)
{
if (m >= n)
{
return 1;
}
--n;
return (2ULL << n) + pow2sum_min(n, m);
}
#define LN(x) (log2(x)/log2(M_E))
int main(int argc, char** argv)
{
if (2 >= argc)
{
fprintf(stderr, "%s a b\n", argv[0]);
exit(EXIT_FAILURE);
}
long a = atol(argv[1]), b = atol(argv[2]);
if (0L >= a || 0L >= b || b < a)
{
puts("Na ...!");
exit(EXIT_FAILURE);
}
/* Expand intevall to cover full dimensions: */
unsigned long long a_c = pow(2, floor(log2(a)));
unsigned long long b_c = pow(2, floor(log2(b+1)) + 1);
double log2_a_c = log2(a_c);
double log2_b_c = log2(b_c);
unsigned long p2s = pow2sum_min(log2_b_c, log2_a_c) - 1;
/* Integral log2(x) between a_c and b_c: */
double A = ((b_c * (LN(b_c) - 1))
- (a_c * (LN(a_c) - 1)))/LN(2)
+ (b+1 - a);
/* "Integer"-integral - integral of log2(x)'s inverse function (2**x) between log(a_c) and log(b_c): */
double D = p2s - (b_c - a_c)/LN(2);
/* Corrective from a_c/b_c to a/b : */
double C = (log2_b_c - 1)*(b_c - (b+1)) + log2_a_c*(a - a_c);
printf("Total used digits: %lld\n", (long long) ((A - D - C) +.5));
}
:-)
The main thing here is the number and kind of iterations done.
Number is
log(floor(b_c)) - log(floor(a_c))
times
doing one
n - 1 /* Integer decrement */
2**n + s /* One bit-shift and one integer addition */
for each iteration.
Here's an entirely look-up based approach. You don't even need the log2 :)
Algorithm
First we precompute interval limits where the number of bits would change and create a lookup table. In other words we create an array limits[2^n], where limits[i] gives us the biggest integer that can be represented with (i+1) bits. Our array is then {1, 3, 7, ..., 2^n-1}.
Then, when we want to determine the sum of bits for our range, we must first match our range limits a and b with the smallest index for which a <= limits[i] and b <= limits[j] holds, which will then tell us that we need (i+1) bits to represent a, and (j+1) bits to represent b.
If the indexes are the same, then the result is simply (b-a+1)*(i+1), otherwise we must separately get the number of bits from our value to the edge of same number of bits interval, and add up total number of bits for each interval between as well. In any case, simple arithmetic.
Code
#include <stdio.h>
#include <limits.h>
#include <time.h>
unsigned long bitsnumsum(unsigned int a, unsigned int b)
{
// generate lookup table
// limits[i] is the max. number we can represent with (i+1) bits
static const unsigned int limits[32] =
{
#define LTN(n) n*2u-1, n*4u-1, n*8u-1, n*16u-1, n*32u-1, n*64u-1, n*128u-1, n*256u-1
LTN(1),
LTN(256),
LTN(256*256),
LTN(256*256*256)
};
// make it work for any order of arguments
if (b < a) {
unsigned int c = a;
a = b;
b = c;
}
// find interval of a
unsigned int i = 0;
while (a > limits[i]) {
++i;
}
// find interval of b
unsigned int j = i;
while (b > limits[j]) {
++j;
}
// add it all up
unsigned long sum = 0;
if (i == j) {
// a and b in the same range
// conveniently, this also deals with j == 0
// so no danger to do [j-1] below
return (i+1) * (unsigned long)(b - a + 1);
}
else {
// add sum of digits in range [a, limits[i]]
sum += (i+1) * (unsigned long)(limits[i] - a + 1);
// add sum of digits in range [limits[j], b]
sum += (j+1) * (unsigned long)(b - limits[j-1]);
// add sum of digits in range [limits[i], limits[j]]
for (++i; i<j; ++i) {
sum += (i+1) * (unsigned long)(limits[i] - limits[i-1]);
}
return sum;
}
}
int main(void)
{
clock_t start, end;
unsigned int a=0, b=UINT_MAX;
start = clock();
printf("Sum of binary digits for numbers in range "
"[%u, %u]: %lu\n", a, b, bitsnumsum(a, b));
end = clock();
printf("Time: %fs\n", ((double)(end-start))/CLOCKS_PER_SEC);
}
Output
$ ./lookup
Sum of binary digits for numbers in range [0, 4294967295]: 133143986178
Time: 0.000282s
Algorithm
The main idea is to find the n2 = log2(x) rounded down. That is the number of digits in x. Let pow2 = 1 << n2. n2 * (pow2 - x + 1) is the number of digits in the values [x...pow2]. Now find the sun of digits in the powers of 2 from 1 to n2-1
Code
I am certain various simplifications can be made.
Untested code. Will review later.
// Let us use unsigned for everything.
unsigned ulog2(unsigned value) {
unsigned result = 0;
if (0xFFFF0000u & value) {
value >>= 16; result += 16;
}
if (0xFF00u & value) {
value >>= 8; result += 8;
}
if (0xF0u & value) {
value >>= 4; result += 4;
}
if (0xCu & value) {
value >>= 2; result += 2;
}
if (0x2 & value) {
value >>= 1; result += 1;
}
return result;
}
unsigned bit_count_helper(unsigned x) {
if (x == 0) {
return 1;
}
unsigned n2 = ulog2(x);
unsigned pow2 = 1u << n;
unsigned sum = n2 * (pow2 - x + 1u); // value from pow2 to x
while (n2 > 0) {
// ... + 5*16 + 4*8 + 3*4 + 2*2 + 1*1
pow2 /= 2;
sum += n2 * pow2;
}
return sum;
}
unsigned bit_count(unsigned a, unsigned b) {
assert(a < b);
return bit_count_helper(b - 1) - bit_count_helper(a);
}
For this problem your solution is the simplest, the one called "naive" where you look for every element in the sequence or in your case interval for check something or execute operations.
Naive Algorithm
Assuming that a and b are positive integers with b greater than a let's call the dimension/size of the interval [a,b], n = (b-a).
Having our number of elements n and using some notations of algorithms (like big-O notation link), the worst case cost is O(n*(numberOfBits_cost)).
From this we can see that we can speed up our algorithm by using a faster algorithm for computing numberOfBits() or we need to find a way to not look at every element of the interval that costs us n operations.
Intuition
Now looking at a possible interval [6,14] you can see that for 6 and 7 we need 3 digits, with 4 need for 8,9,10,11,12,13,14. This results in calling numberOfBits() for every number that use the same number of digits to be represented, while the following multiplication operation would be faster:
(number_in_subinterval)*digitsForThisInterval
((14-8)+1)*4 = 28
((7-6)+1)*3 = 6
So we reduced the looping on 9 elements with 9 operations to only 2.
So writing a function that use this intuition will give us a more efficient in time, not necessarily in memory, algorithm. Using your numberOfBits() function I have created this solution:
int intuitionSol(int a, int b){
int digitsForA = numberOfBits(a);
int digitsForB = numberOfBits(b);
if(digitsForA != digitsForB){
//because a or b can be that isn't the first or last element of the
// interval that a specific number of digit can rappresent there is a need
// to execute some correction operation before on a and b
int tmp = pow(2,digitsForA) - a;
int result = tmp*digitsForA; //will containt the final result that will be returned
int i;
for(i = digitsForA + 1; i < digitsForB; i++){
int interval_elements = pow(2,i) - pow(2,i-1);
result = result + ((interval_elements) * i);
//printf("NumOfElem: %i for %i digits; sum:= %i\n", interval_elements, i, result);
}
int tmp1 = ((b + 1) - pow(2,digitsForB-1));
result = result + tmp1*digitsForB;
return result;
}
else {
int elements = (b - a) + 1;
return elements * digitsForA; // or digitsForB
}
}
Let's look at the cost, this algorithm costs is the cost of doing correction operation on a and b plus the most expensive one that of the for-loop. In my solution however I'm not looping over all elements but only on numberOfBits(b)-numberOfBits(a) that in the worst case, when [0,n], become log(n)-1 thats equivalent to O(log n).
To resume we passed from a linear operations cost O(n) to a logartmic one O(log n) in the worst case. Look on this diagram the diferinces between the two.
Note
When I talk about interval or sub-interval I refer to the interval of elements that use the same number of digits to represent the number in binary.
Following there are some output of my tests with the last one that shows the difference:
Considered interval is [0,4]
YourSol: 9 in time: 0.000015s
IntuitionSol: 9 in time: 0.000007s
Considered interval is [0,0]
YourSol: 1 in time: 0.000005s
IntuitionSol: 1 in time: 0.000005s
Considered interval is [4,7]
YourSol: 12 in time: 0.000016s
IntuitionSol: 12 in time: 0.000005s
Considered interval is [2,123456]
YourSol: 1967697 in time: 0.005010s
IntuitionSol: 1967697 in time: 0.000015s
Suppose I have an array of bytes from a secure PRNG, and I need to generate a number between 1 and 10 using that data, how would I do that correctly?
Think of the array as one big unsigned integer. Then the answer is simple:
(Big_Number % 10) + 1
So all that is needed is a method to find the modulus 10 of big integers. Using modular exponentiation:
#include <limits.h>
#include <stdlib.h>
int ArrayMod10(const unsigned char *a, size_t n) {
int mod10 = 0;
int base = (UCHAR_MAX + 1) % 10;
for (size_t i = n; i-- > 0; ) {
mod10 = (base*mod10 + a[i]) % 10;
base = (base * base) % 10;
}
return mod10;
}
void test10(size_t n) {
unsigned char a[n];
// fill array with your secure PRNG
for (size_t i = 0; i<n; i++) a[i] = rand();
return ArrayMod10(a, n) + 1;
}
There will be a slight bias as 256^n is not a power of 10. With large n, this will rapidly decrease in significance.
Untested code: Detect if a biased result occurred. Calling code could repeatedly call this function with new a array values to get an unbiased result on the rare occasions when bias occurs.
int ArrayMod10BiasDetect(const unsigned char *a, size_t n, bool *biasptr) {
bool bias = true;
int mod10 = 0;
int base = (UCHAR_MAX + 1) % 10; // Note base is usually 6: 256%10, 65536%10, etc.
for (size_t i = n; i-- > 0; ) {
mod10 = (base*mod10 + a[i]) % 10;
if (n > 0) {
if (a[i] < UCHAR_MAX) bias = false;
} else {
if (a[i] < UCHAR_MAX + 1 - base) bias = false;
}
base = (base * base) % 10;
}
*biaseptr = bias;
return mod10;
}
As per the comments follow-up, it seems what you need is modulus operator [%].
You may also need to check the related wiki.
Note: Every time we use the modulo operator on a random number, there is a probability that we'll be running into modulo bias, which ends up in disbalancing the fair distribution of random numbers. You've to take care of that.
For a detailed discussion on this, please see this question and related answers.
It depends on a bunch of things. Secure PRNG sometimes makes long byte arrays instead of integers, let's say it is 16 bytes long array, then extract 32 bit integer like so: buf[0]*0x1000000+buf[1]*0x10000+buf[2]*0x100+buf[3] or use shift operator. This is random so big-endian/little-endian doesn't matter.
char randbytes[16];
//...
const char *p = randbytes;
//assumes size of int is 4
unsigned int rand1 = p[0] << 24 + p[1] << 16 + p[2] << 8 + p[3]; p += 4;
unsigned int rand2 = p[0] << 24 + p[1] << 16 + p[2] << 8 + p[3]; p += 4;
unsigned int rand3 = p[0] << 24 + p[1] << 16 + p[2] << 8 + p[3]; p += 4;
unsigned int rand4 = p[0] << 24 + p[1] << 16 + p[2] << 8 + p[3];
Then use % on the integer
ps, I think that's a long answer. If you want number between 1 and 10 then just use % on first byte.
OK, so this answer is in Java until I get to my Eclipse C/C++ IDE:
public final static int simpleBound(Random rbg, int n) {
final int BYTE_VALUES = 256;
// sanity check, only return positive numbers
if (n <= 0) {
throw new IllegalArgumentException("Oops");
}
// sanity check: choice of value 0 or 0...
if (n == 1) {
return 0;
}
// sanity check: does not fit in byte
if (n > BYTE_VALUES) {
throw new IllegalArgumentException("Oops");
}
// optimization for n = 2^y
if (Integer.bitCount(n) == 1) {
final int mask = n - 1;
return retrieveRandomByte(rbg) & mask;
}
// you can skip to this if you are sure n = 10
// z is upper bound, and contains floor(z / n) blocks of n values
final int z = (BYTE_VALUES / n) * n;
int x;
do {
x = retrieveRandomByte(rbg);
} while (x >= z);
return x % n;
}
So n is the maximum value in a range [0..n), i.e. n is exclusive. For a range [1..10] simply increase the result with 1.
I have to write a program that can calculate the powers of 2 power 2010 and to find the sum of the digits. eg:
if `2 power 12 => gives 4096 . So 4+0+9+6 = 19 .
Now i need to find the same for 2 power 2010.
Please help me to understand.
Here's something to get you started:
char buf[2010]; // 2^2010 < 10^2010 by a huge margin, so buffer size is safe
snprintf(buf, sizeof buf, "%.0Lf", 0x1p2010L);
You have to either use a library that supplies unlimited integer length types (see http://en.wikipedia.org/wiki/Bignum ), or implement a solution that does not need them (e.g. use a digit array and implement the power calculation on the array yourself, which in your case can be as simple as addition in a loop). Since this is homework, probably the latter.
Knowing 2^32, how would you calculate 2^33 with pen and paper?
2^32 is 4294967296
4294967296
* 2
----------
8589934592
8589934592 is 2^33; sum of digits is 8+5+8+9+...+9+2 (62)
Just be aware that 2^2011 is a number with more than 600 digits: not that many to do by computer
GMP is perhaps the best, fastest free multi-architecture library for this. It provides a solid foundation for such calculations, including not only addition, but parsing from strings, multiplication, division, scientific operations, etc.
For literature on the algorithms themselves, I highly recommend The Art of Computer Programming, Volume 2: Seminumerical Algorithms by Donald Knuth. This book is considered by many to be the best single reference for the topic. This book explains from the ground up how such arithmetic can take place on a machine that can only do 32-bit arithmetic.
If you want to implement this calculation from scratch without using any tools, the following code block requires requires only the following additional methods to be supplied:
unsigned int divModByTen(unsigned int *num, unsigned int length);
bool isZero(unsigned int *num, unsigned int length);
divModByTen should divide replace num in memory with the value of num / 10, and return the remainder. The implementation will take some effort, unless a library is used. isZero just checks if the number is all zero's in memory. Once we have these, we can use the following code sample:
unsigned int div10;
int decimalDigitSum;
unsigned int hugeNumber[64];
memset(twoPow2010, 0, sizeof(twoPow2010));
twoPow2010[63] = 0x4000000;
// at this point, twoPow2010 is 2^2010 encoded in binary stored in memory
decimalDigitSum = 0;
while (!izZero(hugeNumber, 64)) {
mod10 = divModByTen(&hugeNumber[0], 64);
decimalDigitSum += mod10;
}
printf("Digit Sum:%d", decimalDigitSum);
This takes only a few lines of code in Delphi... :)
So in c must be the same or shorter.
function PowerOf2(exp: integer): string;
var
n : integer;
Digit : integer;
begin
result := '1';
while exp <> 0 do
begin
Digit := 0;
for n := Length(result) downto 1 do
begin
Digit := (ord(result[n]) - ord('0')) * 2 + Digit div 10;
result[n] := char(Digit mod 10 + ord('0'))
end;
if Digit > 9 then
result := '1' + result;
dec(exp);
end;
end;
-----EDIT-----
This is 1-to-1 c# version.
string PowerOf2(int exp)
{
int n, digit;
StringBuilder result = new StringBuilder("1");
while (exp != 0)
{
digit = 0;
for (n = result.Length; n >= 1; n--)
{
digit = (result[n-1] - '0') * 2 + digit / 10;
result[n-1] = Convert.ToChar(digit % 10 + '0');
}
if (digit > 9)
{
result = new StringBuilder("1" + result.ToString());
}
exp--;
}
return result.ToString();
}
int Sum(string s)
{
int sum = 0;
for (int i = 0; i < s.Length; i++)
{
sum += s[i] - '0';
}
return sum;
}
for (int i = 1; i < 20; i++)
{
string s1s = PowerOf2(i);
int sum = Sum(s1s);
Console.WriteLine(s1s + " --> " + sum);
}
Here's how you can calculate and print 22010:
#include <stdio.h>
#include <string.h>
void AddNumbers(char* dst, const char* src)
{
char ddigit;
char carry = 0;
while ((ddigit = *dst) != '\0')
{
char sdigit = '0';
if (*src != '\0')
{
sdigit = *src++;
}
ddigit += sdigit - '0' + carry;
if (ddigit > '9')
{
ddigit -= 10;
carry = 1;
}
else
{
carry = 0;
}
*dst++ = ddigit;
}
}
void ReverseString(char* s)
{
size_t i, n = strlen(s);
for (i = 0; i < n / 2; i++)
{
char t = s[i];
s[i] = s[n - 1 - i];
s[n - 1 - i] = t;
}
}
int main(void)
{
char result[607], tmp[sizeof(result)];
int i;
memset (result, '0', sizeof(result));
result[0] = '1';
result[sizeof(result) - 1] = '\0';
for (i = 0; i < 2010; i++)
{
memcpy(tmp, result, sizeof(result));
AddNumbers(result, tmp);
}
ReverseString(result);
printf("%s\n", result);
return 0;
}
You can now sum up the individual digits.
I have an array of unsigned chars in c I am trying to print in base 10, and I am stuck. I think this will be better explained in code, so, given:
unsigned char n[3];
char[0] = 1;
char[1] = 2;
char[2] = 3;
I would like to print 197121.
This is trivial with small base 256 arrays. One can simply 1 * 256 ^ 0 + 2 * 256 ^ 1 + 3 * 256 ^ 2.
However, if my array was 100 bytes large, then this quickly becomes a problem. There is no integral type in C that is 100 bytes large, which is why I'm storing numbers in unsigned char arrays to begin with.
How am I supposed to efficiently print this number out in base 10?
I am a bit lost.
There's no easy way to do it using only the standard C library. You'll either have to write the function yourself (not recommended), or use an external library such as GMP.
For example, using GMP, you could do:
unsigned char n[100]; // number to print
mpz_t num;
mpz_import(num, 100, -1, 1, 0, 0, n); // convert byte array into GMP format
mpz_out_str(stdout, 10, num); // print num to stdout in base 10
mpz_clear(num); // free memory for num
When I saw this question, I purpose to solve it, but at that moment I was very busy.
This last weekend I've could gain some prize hours of free time so I considered my pending challenge.
First of all, I suggest you to considered above response. I never use GMP library but I'm sure that it's better solution than a handmade code.
Also, you could be interest to analyze code of bc calculator; it can works with big numbers and I used to test my own code.
Ok, if you are still interested in a code do it by yourself (only with support C language and Standard C library) may be I can give you something.
Before all, a little bit theory. In basic numeric theory (modular arithmetic level) theres is an algorithm that inspire me to arrive at one solution; Multiply and Power algorithm to solve a^N module m:
Result := 1;
for i := k until i = 0
if n_i = 1 then Result := (Result * a) mod m;
if i != 0 then Result := (Result * Result) mod m;
end for;
Where k is number of digits less one of N in binary representation, and n_i is i binary digit. For instance (N is exponent):
N = 44 -> 1 0 1 1 0 0
k = 5
n_5 = 1
n_4 = 0
n_3 = 1
n_2 = 1
n_1 = 0
n_0 = 0
When we make a module operation, as an integer division, we can lose part of the number, so we only have to modify algorithm to don't miss relevant data.
Here is my code (take care that it is an adhoc code, strong dependency of may computer arch. Basically I play with data length of C language so, be carefully because my data length could not be the same):
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
enum { SHF = 31, BMASK = 0x1 << SHF, MODULE = 1000000000UL, LIMIT = 1024 };
unsigned int scaleBigNum(const unsigned short scale, const unsigned int lim, unsigned int *num);
unsigned int pow2BigNum(const unsigned int lim, unsigned int *nsrc, unsigned int *ndst);
unsigned int addBigNum(const unsigned int lim1, unsigned int *num1, const unsigned int lim2, unsigned int *num2);
unsigned int bigNum(const unsigned short int base, const unsigned int exp, unsigned int **num);
int main(void)
{
unsigned int *num, lim;
unsigned int *np, nplim;
int i, j;
for(i = 1; i < LIMIT; ++i)
{
lim = bigNum(i, i, &num);
printf("%i^%i == ", i, i);
for(j = lim - 1; j > -1; --j)
printf("%09u", num[j]);
printf("\n");
free(num);
}
return 0;
}
/*
bigNum: Compute number base^exp and store it in num array
#base: Base number
#exp: Exponent number
#num: Pointer to array where it stores big number
Return: Array length of result number
*/
unsigned int bigNum(const unsigned short int base, const unsigned int exp, unsigned int **num)
{
unsigned int m, lim, mem;
unsigned int *v, *w, *k;
//Note: mem has the exactly amount memory to allocate (dinamic memory version)
mem = ( (unsigned int) (exp * log10( (float) base ) / 9 ) ) + 3;
v = (unsigned int *) malloc( mem * sizeof(unsigned int) );
w = (unsigned int *) malloc( mem * sizeof(unsigned int) );
for(m = BMASK; ( (m & exp) == 0 ) && m; m >>= 1 ) ;
v[0] = (m) ? 1 : 0;
for(lim = 1; m > 1; m >>= 1)
{
if( exp & m )
lim = scaleBigNum(base, lim, v);
lim = pow2BigNum(lim, v, w);
k = v;
v = w;
w = k;
}
if(exp & 0x1)
lim = scaleBigNum(base, lim, v);
free(w);
*num = v;
return lim;
}
/*
scaleBigNum: Make an (num[] <- scale*num[]) big number operation
#scale: Scalar that multiply big number
#lim: Length of source big number
#num: Source big number (array of unsigned int). Update it with new big number value
Return: Array length of operation result
Warning: This method can write in an incorrect position if we don't previous reallocate num (if it's necessary). bigNum method do it for us
*/
unsigned int scaleBigNum(const unsigned short scale, const unsigned int lim, unsigned int *num)
{
unsigned int i;
unsigned long long int n, t;
for(n = 0, t = 0, i = 0; i < lim; ++i)
{
t = (n / MODULE);
n = ( (unsigned long long int) scale * num[i] );
num[i] = (n % MODULE) + t; // (n % MODULE) + t always will be smaller than MODULE
}
num[i] = (n / MODULE);
return ( (num[i]) ? lim + 1 : lim );
}
/*
pow2BigNum: Make a (dst[] <- src[] * src[]) big number operation
#lim: Length of source big number
#src: Source big number (array of unsigned int)
#dst: Destination big number (array of unsigned int)
Return: Array length of operation result
Warning: This method can write in an incorrect position if we don't previous reallocate num (if it's necessary). bigNum method do it for us
*/
unsigned int pow2BigNum(const unsigned int lim, unsigned int *src, unsigned int *dst)
{
unsigned int i, j;
unsigned long long int n, t;
unsigned int k, c;
for(c = 0, dst[0] = 0, i = 0; i < lim; ++i)
{
for(j = i, n = 0; j < lim; ++j)
{
n = ( (unsigned long long int) src[i] * src[j] );
k = i + j;
if(i != j)
{
t = 2 * (n % MODULE);
n = 2 * (n / MODULE);
// (i + j)
dst[k] = ( (k > c) ? ((c = k), 0) : dst[k] ) + (t % MODULE);
++k; // (i + j + 1)
dst[k] = ( (k > c) ? ((c = k), 0) : dst[k] ) + ( (t / MODULE) + (n % MODULE) );
++k; // (i + j + 2)
dst[k] = ( (k > c) ? ((c = k), 0) : dst[k] ) + (n / MODULE);
}
else
{
dst[k] = ( (k > c) ? ((c = k), 0) : dst[k] ) + (n % MODULE);
++k; // (i + j)
dst[k] = ( (k > c) ? ((c = k), 0) : dst[k] ) + (n / MODULE);
}
for(k = i + j; k < (lim + j); ++k)
{
dst[k + 1] += (dst[k] / MODULE);
dst[k] %= MODULE;
}
}
}
i = lim << 1;
return ((dst[i - 1]) ? i : i - 1);
}
/*
addBigNum: Make a (num2[] <- num1[] + num2[]) big number operation
#lim1: Length of source num1 big number
#num1: First source operand big number (array of unsigned int). Should be smaller than second
#lim2: Length of source num2 big number
#num2: Second source operand big number (array of unsigned int). Should be equal or greater than first
Return: Array length of operation result or 0 if num1[] > num2[] (dosen't do any op)
Warning: This method can write in an incorrect position if we don't previous reallocate num2
*/
unsigned int addBigNum(const unsigned int lim1, unsigned int *num1, const unsigned int lim2, unsigned int *num2)
{
unsigned long long int n;
unsigned int i;
if(lim1 > lim2)
return 0;
for(num2[lim2] = 0, n = 0, i = 0; i < lim1; ++i)
{
n = num2[i] + num1[i] + (n / MODULE);
num2[i] = n % MODULE;
}
for(n /= MODULE; n; ++i)
{
num2[i] += n;
n = (num2[i] / MODULE);
}
return (lim2 > i) ? lim2 : i;
}
To compile:
gcc -o bgn <name>.c -Wall -O3 -lm //Math library if you wants to use log func
To check result, use direct output as and input to bc. Easy shell script:
#!/bin/bash
select S in ` awk -F '==' '{print $1 " == " $2 }' | bc`;
do
0;
done;
echo "Test Finished!";
We have and array of unsigned int (4 bytes) where we store at each int of array a number of 9 digits ( % 1000000000UL ); hence num[0] we will have the first 9 digits, num[1] we will have digit 10 to 18, num[2]...
I use convencional memory to work but an improvement can do it with dinamic memory. Ok, but how length It could be the array? (or how many memory we need to allocate?). Using bc calculator (bc -l with mathlib) we can determine how many digits has a number:
l(a^N) / l(10) // Natural logarith to Logarithm base 10
If we know digits, we know amount integers we needed:
( l(a^N) / (9 * l(10)) ) + 1 // Truncate result
If you work with value such as (2^k)^N you can resolve it logarithm with this expression:
( k*N*l(2)/(9*l(10)) ) + 1 // Truncate result
to determine the exactly length of integer array. Example:
256^800 = 2^(8*800) ---> l(2^(8*800))/(9*l(10)) + 1 = 8*800*l(2)/(9*l(10)) + 1
The value 1000000000UL (10^9) constant is very important. A constant like 10000000000UL (10^10) dosen't work because can produce and indetected overflow (try what's happens with number 16^16 and 10^10 constant) and a constant more little such as 1000000000UL (10^8) are correct but we need to reserve more memory and do more steps. 10^9 is key constant for unsigned int of 32 bits and unsigned long long int of 64 bits.
The code has two parts, Multiply (easy) and Power by 2 (more hard). Multiply is just multiplication and scale and propagate the integer overflow. It take the principle of associative property in math to do exactly the inverse principle, so if k(A + B + C) we want kA + kB + kC where number will be k*A*10^18 + k*B*10^9 + kC. Obiously, kC operation can generate a number bigger than 999 999 999, but never more bigger than 0xFF FF FF FF FF FF FF FF. A number bigger than 64 bits can never occur in a multiplication because C is an unsigned integer of 32 bits and k is a unsigned short of 16 bits. In worts case, we will have this number:
k = 0x FF FF;
C = 0x 3B 9A C9 FF; // 999999999
n = k*C = 0x 3B 9A | 8E 64 36 01;
n % 1000000000 = 0x 3B 99 CA 01;
n / 1000000000 = 0x FF FE;
After Mul kB we need to add 0x FF FE from last multiplication of C ( B = kB + (C / module) ), and so on (we have 18 bits arithmetic offset, enough to guarantee correct values).
Power is more complex but is in essencial, the same problem (multiplication and add), so I give some tricks about code power:
Data types are important, very important
If you try to multiplication an unsigned integer with unsigned integer, you get another unsigned integer. Use explicit cast to get unsigned long long int and don't lose data.
Always use unsigned modifier, dont forget it!
Power by 2 can directly modify 2 index ahead of current index
gdb is your friend
I've developed another method that add big numbers. These last I don't prove so much but I think it works well. Don't be cruels with me if it has a bug.
...and that's all!
PD1: Developed in a
Intel(R) Pentium(R) 4 CPU 1.70GHz
Data length:
unsigned short: 2
unsigned int: 4
unsigned long int: 4
unsigned long long int: 8
Numbers such as 256^1024 it spend:
real 0m0.059s
user 0m0.033s
sys 0m0.000s
A bucle that's compute i^i where i goes to i = 1 ... 1024:
real 0m40.716s
user 0m14.952s
sys 0m0.067s
For numbers such as 65355^65355, spent time is insane.
PD2: My response is so late but I hope my code it will be usefull.
PD3: Sorry, explain me in english is one of my worst handicaps!
Last update: I just have had an idea that with same algorithm but other implementation, improve response and reduce amount memory to use (we can use the completely bits of unsigned int). The secret: n^2 = n * n = n * (n - 1 + 1) = n * (n - 1) + n.
(I will not do this new code, but if someone are interested, may be after exams... )
I don't know if you still need a solution, but I wrote an article about this problem. It shows a very simple algorithm which can be used to convert an arbitrary long number with base X to a corresponding number of base Y. The algorithm is written in Python, but it is really only a few lines long and doesn't use any Python magic. I needed such an algorithm for a C implementation, too, but decided to describe it using Python for two reasons. First, Python is very readable by anyone who understands algorithms written in a pseudo programming language and, second, I am not allowed to post the C version, because it I did it for my company. Just have a look and you will see how easy this problem can be solved in general. An implementation in C should be straight forward...
Here is a function that does what you want:
#include <math.h>
#include <stddef.h> // for size_t
double getval(unsigned char *arr, size_t len)
{
double ret = 0;
size_t cur;
for(cur = 0; cur < len; cur++)
ret += arr[cur] * pow(256, cur);
return ret;
}
That looks perfectly readable to me. Just pass the unsigned char * array you want to convert and the size. Note that it won't be perfect - for arbitrary precision, I suggest looking into the GNU MP BigNum library, as has been suggested already.
As a bonus, I don't like your storing your numbers in little-endian order, so here's a version if you want to store base-256 numbers in big-endian order:
#include <stddef.h> // for size_t
double getval_big_endian(unsigned char *arr, size_t len)
{
double ret = 0;
size_t cur;
for(cur = 0; cur < len; cur++)
{
ret *= 256;
ret += arr[cur];
}
return ret;
}
Just things to consider.
It may be too late or too irrelevant to make this suggestion, but could you store each byte as two base 10 digits (or one base 100) instead of one base 256? If you haven't implemented division yet, then that implies all you have is addition, subtraction, and maybe multiplication; those shouldn't be too hard to convert. Once you've done that, printing it would be trivial.
As I was not satisfied with the other answers provided, I decided to write an alternative solution myself:
#include <stdlib.h>
#define BASE_256 256
char *largenum2str(unsigned char *num, unsigned int len_num)
{
int temp;
char *str, *b_256 = NULL, *cur_num = NULL, *prod = NULL, *prod_term = NULL;
unsigned int i, j, carry = 0, len_str = 1, len_b_256, len_cur_num, len_prod, len_prod_term;
//Get 256 as an array of base-10 chars we'll use later as our second operand of the product
for ((len_b_256 = 0, temp = BASE_256); temp > 0; len_b_256++)
{
b_256 = realloc(b_256, sizeof(char) * (len_b_256 + 1));
b_256[len_b_256] = temp % 10;
temp = temp / 10;
}
//Our first operand (prod) is the last element of our num array, which we'll convert to a base-10 array
for ((len_prod = 0, temp = num[len_num - 1]); temp > 0; len_prod++)
{
prod = realloc(prod, sizeof(*prod) * (len_prod + 1));
prod[len_prod] = temp % 10;
temp = temp / 10;
}
while (len_num > 1) //We'll stay in this loop as long as we still have elements in num to read
{
len_num--; //Decrease the length of num to keep track of the current element
//Convert this element to a base-10 unsigned char array
for ((len_cur_num = 0, temp = num[len_num - 1]); temp > 0; len_cur_num++)
{
cur_num = (char *)realloc(cur_num, sizeof(char) * (len_cur_num + 1));
cur_num[len_cur_num] = temp % 10;
temp = temp / 10;
}
//Multiply prod by 256 and save that as prod_term
len_prod_term = 0;
prod_term = NULL;
for (i = 0; i < len_b_256; i++)
{ //Repeat this loop 3 times, one for each element in {6,5,2} (256 as a reversed base-10 unsigned char array)
carry = 0; //Set the carry to 0
prod_term = realloc(prod_term, sizeof(*prod_term) * (len_prod + i)); //Allocate memory to save prod_term
for (j = i; j < (len_prod_term); j++) //If we have digits from the last partial product of the multiplication, add it here
{
prod_term[j] = prod_term[j] + prod[j - i] * b_256[i] + carry;
if (prod_term[j] > 9)
{
carry = prod_term[j] / 10;
prod_term[j] = prod_term[j] % 10;
}
else
{
carry = 0;
}
}
while (j < (len_prod + i)) //No remaining elements of the former prod_term, so take only into account the results of multiplying mult * b_256
{
prod_term[j] = prod[j - i] * b_256[i] + carry;
if (prod_term[j] > 9)
{
carry = prod_term[j] / 10;
prod_term[j] = prod_term[j] % 10;
}
else
{
carry = 0;
}
j++;
}
if (carry) //A carry may be present in the last term. If so, allocate memory to save it and increase the length of prod_term
{
len_prod_term = j + 1;
prod_term = realloc(prod_term, sizeof(*prod_term) * (len_prod_term));
prod_term[j] = carry;
}
else
{
len_prod_term = j;
}
}
free(prod); //We don't need prod anymore, prod will now be prod_term
prod = prod_term;
len_prod = len_prod_term;
//Add prod (formerly prod_term) to our current number of the num array, expressed in a b-10 array
carry = 0;
for (i = 0; i < len_cur_num; i++)
{
prod[i] = prod[i] + cur_num[i] + carry;
if (prod[i] > 9)
{
carry = prod[i] / 10;
prod[i] -= 10;
}
else
{
carry = 0;
}
}
while (carry && (i < len_prod))
{
prod[i] = prod[i] + carry;
if (prod[i] > 9)
{
carry = prod[i] / 10;
prod[i] -= 10;
}
else
{
carry = 0;
}
i++;
}
if (carry)
{
len_prod++;
prod = realloc(prod, sizeof(*prod) * len_prod);
prod[len_prod - 1] = carry;
carry = 0;
}
}
str = malloc(sizeof(char) * (len_prod + 1)); //Allocate memory for the return string
for (i = 0; i < len_prod; i++) //Convert the numeric result to its representation as characters
{
str[len_prod - 1 - i] = prod[i] + '0';
}
str[i] = '\0'; //Terminate our string
free(b_256); //Free memory
free(prod);
free(cur_num);
return str;
}
The idea behind it all derives from simple math. For any base-256 number, its base-10 representation can be calculated as:
num[i]*256^i + num[i-1]*256^(i-1) + (···) + num[2]*256^2 + num[1]*256^1 + num[0]*256^0
which expands to:
(((((num[i])*256 + num[i-1])*256 + (···))*256 + num[2])*256 + num[1])*256 + num[0]
So all we have to do is to multiply, step-by step, each element of the number array by 256 and add to it the next element, and so on... That way we can get the base-10 number.