The function must return the count of pairs of numbers in the array songs (integer array consisting of lengths of songs in seconds) such that the pairs formed add up to whole minutes.
long playlist(int songs_count, int *songs) {
int i, j, k = 0;
for (i = 0; i < songs_count; i++) {
for (j = i + 1; j < songs_count; j++)
if ((songs[i] + songs[j]) % 60 == 0)
k++;
}
return k;
}
A first straight forward approach would be like this:
Create an array holding 60 entries with the remainder of seconds%60 initialized to all zeroes.
Calculate the remainder of each song and increment the related entry in the array.
Iterate over all possible remainders (1..29)
For each remainder you have a = array[i] songs and b = array[60-i] matching songs which you need to combine: num = a*b; k += num;
For i==0 and i==30 you need special handling as the matching song is in same array element: num = a*(a-1);
This will reduce the time complexity to O(N):
You need
a loop over n to populate the array (could be done once when building the song list) and
a loop over 0..30 for the calculation.
This results in O(N)+O(1)
Edit: Depending on your rules (does order of the songs matter) you might need to multiply with 2.
If the value of seconds is less, you can use hash map (unordered_map in c++) to solve it in ~O(n) complexity.
Suppose you know that the maximum value of the pair is 600 secs, then you can have another array storing these values.
for(int i = 1; i <= 10; i++)
{
a[i-1] = i * 60;
}
//a[] = {60, 120, 180, 240, 300, 360, 420, 480, 540, 600};
unordered_map <int, int> mymap;
for(int i = 0; i < songs_count; i++)
{
mymap[i] = songs[i];
}
Now you can modify your code as (Solution is for C++):
long playlist(int songs_count, int *songs)
{
int i, j, k = 0;
for(int i = 1; i <= 10; i++)
{
a[i-1] = i * 60;
}
//a[] = {60, 120, 180, 240, 300, 360, 420, 480, 540, 600};
unordered_map <int, int> mymap;
for(int i = 0; i < songs_count; i++)
{
mymap[i] = songs[i];
}
for (i = 0; i < songs_count; i++)
{
for (j = 0; j < n /*size of a*/; j++)
{
if (a[j] > songs[i] && mymap.find(a[j] - songs[i]) != mymap.end())
{
k++;
}
}
}
return k;
}
You can bring down the complexity from O(N2) to O(N) with a simple approach:
using an array int a[60], for each i in 0..59, compute the number of songs whose duration has i seconds and a whole number of minutes. A single pass is sufficient.
for each i in the array, compute the number of pairs of songs of interest. again a single pass is needed.
to compute the number of pairs, one needs extra specification:
are pairs ordered?
can a pair have twice the same song?
Assuming a pair must have different songs and order does not matter, here is an implementation:
long playlist(int songs_count, int *songs) {
long a[60] = { 0 };
int i;
long total;
for (i = 0; i < songs_count; i++)
a[songs[i] % 60]++;
total = (a[0] * (a[0] - 1) + a[30] * (a[30] - 1)) / 2;
for (i = 1; i <= 29; i++)
total += a[i] * a[60 - i];
return total;
}
If order matters and pairs can have twice the same song, the computation is different:
long playlist(int songs_count, int *songs) {
long a[60] = { 0 };
int i;
long total;
for (i = 0; i < songs_count; i++)
a[songs[i] % 60]++;
total = 0;
for (i = 0; i < 60; i++)
total += a[i] * a[(60 - i) % 60];
return total;
}
Related
I'm trying to write a program that will sort an array of 20 random numbers by the sums of their digits.
For example:
"5 > 11" because 5 > 1+1 (5 > 2).
I managed to sort the sums but is it possible to return to the original numbers or do it other way?
#include <stdio.h>
void sortujTab(int tab[], int size){
int sum,i;
for(int i=0;i<size;i++)
{
while(tab[i]>0){//sum as added digits of an integer
int p=tab[i]%10;
sum=sum+p;
tab[i]/=10;
}
tab[i]=sum;
sum=0;
}
for(int i=0;i<size;i++)//print of unsorted sums
{
printf("%d,",tab[i]);
}
printf("\n");
for(int i=0;i<size;i++)//sorting sums
for(int j=i+1;j<=size;j++)
{
if(tab[i]>tab[j]){
int temp=tab[j];
tab[j]=tab[i];
tab[i]=temp;
}
}
for(int i=0;i<20;i++)//print of sorted sums
{
printf("%d,",tab[i]);
}
}
int main()
{
int tab[20];
int size=sizeof(tab)/sizeof(*tab);
for(int i=0;i<=20;i++)
{
tab[i]=rand()%1000;// assamble the value
}
for(int i=0;i<20;i++)
{
printf("%d,",tab[i]);//print unsorted
}
printf("\n");
sortujTab(tab,size);
return 0;
}
There are two basic approach :
Create a function that return the sum for an integer, say sum(int a), then call it on comparison, so instead of tab[i] > tab [j] it becomes sum(tab[i]) > sum (tab[j])
Store the sum into a different array, compare with the new array, and on swapping, swap both the original and the new array
The first solution works well enough if the array is small and takes no extra memory, while the second solution didn't need to repeatedly calculate the sum. A caching approach is also possible with map but it's only worth it if there are enough identical numbers in the array.
Since your numbers are non-negative and less than 1000, you can encode the sum of the digits in the numbers itself. So, this formula will be true: encoded_number = original_number + 1000 * sum_of_the_digits. encoded_number/1000 will decode the sum of the digits, and encoded_number%1000 will decode the original number. Follow the modified code below. The numbers enclosed by parentheses in the output are original numbers. I've tried to modify minimally your code.
#include <stdio.h>
#include <stdlib.h>
void sortujTab(int tab[], int size)
{
for (int i = 0; i < size; i++) {
int sum = 0, n = tab[i];
while (n > 0) { //sum as added digits of an integer
int p = n % 10;
sum = sum + p;
n /= 10;
}
tab[i] += sum * 1000;
}
for (int i = 0; i < size; i++) { //print of unsorted sums
printf("%d%c", tab[i] / 1000, i < size - 1 ? ',' : '\n');
}
for (int i = 0; i < size; i++) { //sorting sums
for (int j = i + 1; j < size; j++) {
if (tab[i] / 1000 > tab[j] / 1000) {
int temp = tab[j];
tab[j] = tab[i];
tab[i] = temp;
}
}
}
for (int i = 0; i < size; i++) { //print of sorted sums
printf("%d(%d)%c", tab[i] / 1000, tab[i] % 1000, i < size - 1 ? ',' : '\n');
}
}
int main(void)
{
int tab[20];
int size = sizeof(tab) / sizeof(*tab);
for (int i = 0; i < size; i++) {
tab[i] = rand() % 1000; // assamble the value
}
for (int i = 0; i < size; i++) {
printf("%d%c", tab[i], i < size - 1 ? ',' : '\n'); //print unsorted
}
sortujTab(tab, size);
return 0;
}
If the range of numbers doesn't allow such an encoding, then you can declare a structure with two integer elements (one for the original number and one for the sum of its digits), allocate an array for size elements of this structure, and initialize and sort the array using the digit sums as the keys.
You can sort an array of indexes rather than the array with data.
#include <stdio.h>
//poor man's interpretation of sumofdigits() :-)
int sod(int n) {
switch (n) {
default: return 0;
case 5: return 5;
case 11: return 2;
case 1000: return 1;
case 9: return 9;
}
}
void sortbyindex(int *data, int *ndx, int size) {
//setup default indexes
for (int k = 0; k < size; k++) ndx[k] = k;
//sort the indexes
for (int lo = 0; lo < size; lo++) {
for (int hi = lo + 1; hi < size; hi++) {
if (sod(data[ndx[lo]]) > sod(data[ndx[hi]])) {
//swap indexes
int tmp = ndx[lo];
ndx[lo] = ndx[hi];
ndx[hi] = tmp;
}
}
}
}
int main(void) {
int data[4] = {5, 11, 1000, 9};
int ndx[sizeof data / sizeof *data];
sortbyindex(data, ndx, 4);
for (int k = 0; k < sizeof data / sizeof *data; k++) {
printf("%d\n", data[ndx[k]]);
}
return 0;
}
I created a cross-correlation algorithm, and I am trying to maximize its performance by reducing the time it takes for it to run. First of all, I reduced the number of function calls within the "crossCorrelationV2" function. Second, I created several macros at the top of the program for constants. Third, I reduced the number of loops that are inside the "crossCorrelationV2" function. The code that you see is the most recent code that I have.
Are there any other methods I can use to try and reduce the processing time of my code?
Let's assume that I am only focused on the functions "crossCorrelationV2" and "createAnalyzingWave".
I would be glad for any advice, whether in general about programming or pertaining to those two specific functions; I am a beginner programmer. Thanks.
#include <stdio.h>
#include <stdlib.h>
#define ARRAYSIZE 4096
#define PULSESNUMBER 16
#define DATAFREQ 1300
// Print the contents of the array onto the console.
void printArray(double array[], int size){
int k;
for (k = 0; k < size; k++){
printf("%lf ", array[k]);
}
printf("\n");
}
// Creates analyzing square wave. This square wave has unity (1) magnitude.
// The number of high values in each period is determined by high values = (analyzingT/2) / time increment
void createAnalyzingWave(double analyzingFreq, double wave[]){
int highValues = (1 / analyzingFreq) * 0.5 / ((PULSESNUMBER * (1 / DATAFREQ) / ARRAYSIZE));
int counter = 0;
int p;
for(p = 1; p <= ARRAYSIZE; p++){
if ((counter % 2) == 0){
wave[p - 1] = 1;
} else{
wave[p - 1] = 0;
}
if (p % highValues == 0){
counter++;
}
}
}
// Creates data square wave (for testing purposes, for the real implementation actual ADC data will be used). This
// square wave has unity magnitude.
// The number of high values in each period is determined by high values = array size / (2 * number of pulses)
void createDataWave(double wave[]){
int highValues = ARRAYSIZE / (2 * PULSESNUMBER);
int counter = 0;
int p;
for(p = 0; p < ARRAYSIZE; p++){
if ((counter % 2) == 0){
wave[p] = 1;
} else{
wave[p] = 0;
}
if ((p + 1) % highValues == 0){
counter++;
}
}
}
// Finds the average of all the values inside an array
double arrayAverage(double array[], int size){
int i;
double sum = 0;
// Same thing as for(i = 0; i < arraySize; i++)
for(i = size; i--; ){
sum = array[i] + sum;
}
return sum / size;
}
// Cross-Correlation algorithm
double crossCorrelationV2(double dataWave[], double analyzingWave[]){
int bigArraySize = (2 * ARRAYSIZE) - 1;
// Expand analyzing array into array of size 2arraySize-1
int lastArrayIndex = ARRAYSIZE - 1;
int lastBigArrayIndex = 2 * ARRAYSIZE - 2; //bigArraySize - 1; //2 * arraySize - 2;
double bigAnalyzingArray[bigArraySize];
int i;
int b;
// Set first few elements of the array equal to analyzingWave
// Set remainder of big analyzing array to 0
for(i = 0; i < ARRAYSIZE; i++){
bigAnalyzingArray[i] = analyzingWave[i];
bigAnalyzingArray[i + ARRAYSIZE] = 0;
}
double maxCorrelationValue = 0;
double currentCorrelationValue;
// "Beginning" of correlation algorithm proper
for(i = 0; i < bigArraySize; i++){
currentCorrelationValue = 0;
for(b = lastBigArrayIndex; b > 0; b--){
if (b >= lastArrayIndex){
currentCorrelationValue = dataWave[b - lastBigArrayIndex / 2] * bigAnalyzingArray[b] + currentCorrelationValue;
}
bigAnalyzingArray[b] = bigAnalyzingArray[b - 1];
}
bigAnalyzingArray[0] = 0;
if (currentCorrelationValue > maxCorrelationValue){
maxCorrelationValue = currentCorrelationValue;
}
}
return maxCorrelationValue;
}
int main(){
int samplesNumber = 25;
double analyzingFreq = 1300;
double analyzingWave[ARRAYSIZE];
double dataWave[ARRAYSIZE];
createAnalyzingWave(analyzingFreq, analyzingWave);
//createDataWave(arraySize, pulsesNumber, dataWave);
double maximumCorrelationArray[samplesNumber];
int i;
for(i = 0; i < samplesNumber; i++){
createDataWave(dataWave);
maximumCorrelationArray[i] = crossCorrelationV2(dataWave, analyzingWave);
}
printf("Average of the array values: %lf\n", arrayAverage(maximumCorrelationArray, samplesNumber));
return 0;
}
The first point is that you are explicitly shifting the analizingData array, this way you are required twice as much memory and moving the items is about 50% of your time. In a test here using crossCorrelationV2 takes 4.1 seconds, with the implementation crossCorrelationV3 it runs in ~2.0 seconds.
The next thing is that you are spending time multiplying by zero on the padded array, removing that, and also removing the padding, and simplifying the indices we end with crossCorrelationV4 that makes the program to run in ~1.0 second.
// Cross-Correlation algorithm
double crossCorrelationV3(double dataWave[], double analyzingWave[]){
int bigArraySize = (2 * ARRAYSIZE) - 1;
// Expand analyzing array into array of size 2arraySize-1
int lastArrayIndex = ARRAYSIZE - 1;
int lastBigArrayIndex = 2 * ARRAYSIZE - 2; //bigArraySize - 1; //2 * arraySize - 2;
double bigAnalyzingArray[bigArraySize];
int i;
int b;
// Set first few elements of the array equal to analyzingWave
// Set remainder of big analyzing array to 0
for(i = 0; i < ARRAYSIZE; i++){
bigAnalyzingArray[i] = analyzingWave[i];
bigAnalyzingArray[i + ARRAYSIZE] = 0;
}
double maxCorrelationValue = 0;
double currentCorrelationValue;
// "Beginning" of correlation algorithm proper
for(i = 0; i < bigArraySize; i++){
currentCorrelationValue = 0;
// Instead of checking if b >= lastArrayIndex inside the loop I use it as
// a stopping condition.
for(b = lastBigArrayIndex; b >= lastArrayIndex; b--){
// instead of shifting bitAnalizing[b] = bigAnalyzingArray[b-1] every iteration
// I simply use bigAnalizingArray[b-i]
currentCorrelationValue = dataWave[b - lastBigArrayIndex / 2] * bigAnalyzingArray[b - i] + currentCorrelationValue;
}
bigAnalyzingArray[0] = 0;
if (currentCorrelationValue > maxCorrelationValue){
maxCorrelationValue = currentCorrelationValue;
}
}
return maxCorrelationValue;
}
// Cross-Correlation algorithm
double crossCorrelationV4(double dataWave[], double analyzingWave[]){
int bigArraySize = (2 * ARRAYSIZE) - 1;
// Expand analyzing array into array of size 2arraySize-1
int lastArrayIndex = ARRAYSIZE - 1;
int lastBigArrayIndex = 2 * ARRAYSIZE - 2; //bigArraySize - 1; //2 * arraySize - 2;
// I will not allocate the bigAnalizingArray here
// double bigAnalyzingArray[bigArraySize];
int i;
int b;
// I will not copy the analizingWave to bigAnalyzingArray
// for(i = 0; i < ARRAYSIZE; i++){
// bigAnalyzingArray[i] = analyzingWave[i];
// bigAnalyzingArray[i + ARRAYSIZE] = 0;
// }
double maxCorrelationValue = 0;
double currentCorrelationValue;
// Compute the correlation by symmetric paris
// the idea here is to simplify the indices of the inner loops since
// they are computed more times.
for(i = 0; i < lastArrayIndex; i++){
currentCorrelationValue = 0;
for(b = lastArrayIndex - i; b >= 0; b--){
// instead of shifting bitAnalizing[b] = bigAnalyzingArray[b-1] every iteration
// I simply use bigAnalizingArray[b-i]
currentCorrelationValue += dataWave[b] * analyzingWave[b + i];
}
if (currentCorrelationValue > maxCorrelationValue){
maxCorrelationValue = currentCorrelationValue;
}
if(i != 0){
currentCorrelationValue = 0;
// Correlate shifting to the other side
for(b = lastArrayIndex - i; b >= 0; b--){
// instead of shifting bitAnalizing[b] = bigAnalyzingArray[b-1] every iteration
// I simply use bigAnalizingArray[b-i]
currentCorrelationValue += dataWave[b + i] * analyzingWave[b];
}
if (currentCorrelationValue > maxCorrelationValue){
maxCorrelationValue = currentCorrelationValue;
}
}
}
return maxCorrelationValue;
}
If you want more optimization you can unroll some iterations of the loop and enable some compiler optimizations like vector extension.
I want to find which items are eventually chosen in the optimal solution of the knapsack problem using the method of dynamic programming.
This is my interpretation so far...
#include<stdio.h>
int getMax(int x, int y) {
if(x > y) {
return x;
} else {
return y;
}
}
int main(void) {
//the first element is set to -1 as
//we are storing item from index 1
//in val[] and wt[] array
int val[] = {-1, 100, 20, 60, 40};
int wt[] = {-1, 3, 2, 4, 1};
int A[] = {0,0,0,0,0};
int n = 4; //num
int W = 5;//cap
int i, j;
// value table having n+1 rows and W+1 columns
int V[n+1][W+1];
// fill the row i=0 with value 0
for(j = 0; j <= W; j++) {
V[0][j] = 0;
}
// fill the column w=0 with value 0
for(i = 0; i <= n; i++) {
V[i][0] = 0;
}
//fill the value table
for(i = 1; i <= n; i++) {
for(j = 1; j <= W; j++) {
if(wt[i] <= j) {
V[i][j] = getMax(V[i-1][j], val[i] + V[i-1][j - wt[i]]);
} else {
V[i][j] = V[i-1][j];
}
}
}
//max value that can be put inside the knapsack
printf("Max Value: %d\n", V[n][W]);
//==================================find items
int n1,c;
n1=n;
c=W;
int A2[n1][c];
while(c>0){
if(A2[n1][c]==A2[n1-1][c]){
A[n1]=0;
} else {
A[n1]=1;
}
n1=n1-1;
c=c-wt[n1];
}
printf("Final array of items: ");
for(i = 0; i < n; i++){
printf("%d",A[i]);
}
} // end of main
And this is the output:
Max Value: 140
Final array of items: 0001
This string of ones and zeros is meant to be the finally chosen items, but from the solution this seems to be wrong!
I followed this algorithm:
While the remaining capacity is greater than 0 do
If Table[n, c] = Table[n-1, c] then
Item n has not been included in the optimal solution
Else
Item n has been included in the optimal solution
Process Item n
Move one row up to n-1
Move to column c – weight(n)
So, is this algorithm wrong / not suitable for this method, or am I missing something?
I have spent more 10hr+ on trying to sort the following(hexadecimals) in LSD radix sort, but no avail. There is very little material on this subject on web.
0 4c7f cd80 41fc 782c 8b74 7eb1 9a03 aa01 73f1
I know I have to mask and perform bitwise operations to process each hex digit (4 bits), but have no idea on how and where.
I'm using the code (I understand) from GeeksforGeeks
void rsort(int a[], int n) {
int max = getMax(a, n);
for (int exp = 1; max / exp > 0; exp *= 10) {
ccsort(a, n, exp);
}
}
int getMax(int a[], int n) {
int max = a[0];
int i = 0;
for (i = 0; i < n; i++) {
if (a[i] > max) {
max = a[i];
}
}
return max;
}
void ccsort(int a[], int n, int exp) {
int count[n];
int output[n];
int i = 0;
for (i = 0; i < n; i++) {
count[i] = 0;
output[i] = 0;
}
for (i = 0; i < n; i++) {
++count[(a[i] / exp) % 10];
}
for (i = 1; i <= n; i++) {
count[i] += count[i - 1];
}
for (i = n - 1; i >= 0; i--) {
output[count[(a[i] / exp) % 10] - 1] = a[i];
--count[(a[i] / exp) % 10];
}
for (i = 0; i < n; i++) {
a[i] = output[i];
}
}
I have also checked all of StackOverFlow on this matter, but none of them covers the details.
Your implementation of radix sort is slightly incorrect:
it cannot handle negative numbers
the array count[] in function ccsort() should have a size of 10 instead of n. If n is smaller than 10, the function does not work.
the loop for cumulating counts goes one step too far: for (i = 1; i <= n; i++). Once again the <= operator causes a bug.
you say you sort by hex digits but the code uses decimal digits.
Here is a (slightly) improved version with explanations:
void ccsort(int a[], int n, int exp) {
int count[10] = { 0 };
int output[n];
int i, last;
for (i = 0; i < n; i++) {
// compute the number of entries with any given digit at level exp
++count[(a[i] / exp) % 10];
}
for (i = last = 0; i < 10; i++) {
// update the counts to have the index of the place to dispatch the next
// number with a given digit at level exp
last += count[i];
count[i] = last - count[i];
}
for (i = 0; i < n; i++) {
// dispatch entries at the right index for its digit at level exp
output[count[(a[i] / exp) % 10]++] = a[i];
}
for (i = 0; i < n; i++) {
// copy entries batch to original array
a[i] = output[i];
}
}
int getMax(int a[], int n) {
// find the largest number in the array
int max = a[0];
for (int i = 1; i < n; i++) {
if (a[i] > max) {
max = a[i];
}
}
return max;
}
void rsort(int a[], int n) {
int max = getMax(a, n);
// for all digits required to express the maximum value
for (int exp = 1; max / exp > 0; exp *= 10) {
// sort the array on one digit at a time
ccsort(a, n, exp);
}
}
The above version is quite inefficient because of all the divisions and modulo operations. Performing on hex digits can be done with shifts and masks:
void ccsort16(int a[], int n, int shift) {
int count[16] = { 0 };
int output[n];
int i, last;
for (i = 0; i < n; i++) {
++count[(a[i] >> shift) & 15];
}
for (i = last = 0; i < 16; i++) {
last += count[i];
count[i] = last - count[i];
}
for (i = 0; i < n; i++) {
output[count[(a[i] >> shift) & 15]++] = a[i];
}
for (i = 0; i < n; i++) {
a[i] = output[i];
}
}
void rsort16(int a[], int n) {
int max = a[0];
for (int i = 1; i < n; i++) {
if (a[i] > max) {
max = a[i];
}
}
for (int shift = 0; (max >> shift) > 0; shift += 4) {
ccsort16(a, n, shift);
}
}
It would be approximately twice as fast to sort one byte at a time with a count array of 256 entries. It would also be faster to compute the counts for all digits in one pass, as shown in rcgldr's answer.
Note that this implementation still cannot handle negative numbers.
There's a simpler way to implement a radix sort. After checking for max, find the lowest power of 16 >= max value. This can be done with max >>= 4 in a loop, incrementing x so that when max goes to zero, then 16 to the power x is >= the original max value. For example a max of 0xffff would need 4 radix sort passes, while a max of 0xffffffff would take 8 radix sort passes.
If the range of values is most likely to take the full range available for an integer, there's no need to bother determining max value, just base the radix sort on integer size.
The example code you have shows a radix sort that scans an array backwards due to the way the counts are converted into indices. This can be avoided by using an alternate method to convert counts into indices. Here is an example of a base 256 radix sort for 32 bit unsigned integers. It uses a matrix of counts / indices so that all 4 rows of counts are generated with just one read pass of the array, followed by 4 radix sort passes (so the sorted data ends up back in the original array). std::swap is a C++ function to swap the pointers, for a C program, this can be replaced by swapping the pointers inline. t = a; a = b; b = t, where t is of type uint32_t * (ptr to unsigned 32 bit integer). For a base 16 radix sort, the matrix size would be [8][16].
// a is input array, b is working array
uint32_t * RadixSort(uint32_t * a, uint32_t *b, size_t count)
{
size_t mIndex[4][256] = {0}; // count / index matrix
size_t i,j,m,n;
uint32_t u;
for(i = 0; i < count; i++){ // generate histograms
u = a[i];
for(j = 0; j < 4; j++){
mIndex[j][(size_t)(u & 0xff)]++;
u >>= 8;
}
}
for(j = 0; j < 4; j++){ // convert to indices
m = 0;
for(i = 0; i < 256; i++){
n = mIndex[j][i];
mIndex[j][i] = m;
m += n;
}
}
for(j = 0; j < 4; j++){ // radix sort
for(i = 0; i < count; i++){ // sort by current lsb
u = a[i];
m = (size_t)(u>>(j<<3))&0xff;
b[mIndex[j][m]++] = u;
}
std::swap(a, b); // swap ptrs
}
return(a);
}
void int_radix_sort(void) {
int group; //because extracting 8 bits
int buckets = 1 << 8; //using size 256
int map[buckets];
int mask = buckets - 1;
int i;
int cnt[buckets];
int flag = NULL;
int partition;
int *src, *dst;
for (group = 0; group < 32; group += 8) {
// group = 8, number of bits we want per round, we want 4 rounds
// cnt
for (int i = 0; i < buckets; i++) {
cnt[i] = 0;
}
for (int j = 0; j < n; j++) {
i = (lst[j] >> group) & mask;
cnt[i]++;
tmp[j] = lst[j];
}
//map
map[0] = 0;
for (int i = 1; i < buckets; i++) {
map[i] = map[i - 1] + cnt[i - 1];
}
//move
for (int j = 0; j < n; j++) {
i = (tmp[j] >> group) & mask;
lst[map[i]] = tmp[j];
map[i]++;
}
}
}
After hours of researching I came across the answer. I'm still do not understand what is going on in this code/answer. I cannot get my head wrapped around the concept. Hopefully, someone can explain.
I see your points. I think negative numbers are easy to sort after the list has been sorted with something like loop, flag, and swap. wb unsigned float points? – itproxti Nov 1 '16 at 16:02
As for handling floating points there might be a way, for example 345.768 is the number, it needs to be converted to an integer, i.e. make it 345768, I multiplied 1000 with it. Just like the offset moves the -ve numbers to +ve domain, so will multiplying by 1000, 10000 etc will turn the floats to numbers with their decimal part as all zeros. Then they can be typecasted to int or long. However with large values, the whole reformed number may not be accomodated within the entire int or long range.
The number that is to be multiplied has to be constant, just like the offset so that the relationship among the magnitudes is preserved. Its better to use powers of 2 such as 8 or 16, as then bitshifting operator can be used. However just like the calculation of offset takes some time, so will calculation of the multiplier will take some time. The whole array is to be searched to calculate the least number that when multiplied will turn all the numbers with zeros in decimal parts.
This may not compute fast but still can do the job if required.
for the purpose of smoothing an image, I need to summarize all pixels around a given center pixel and then build the average, what's basically my new pixel then.
There are like two problems now:
1) What's a good way to summarize them? and
2) How can I best avoid the corner pixels?
This is what I did so far, but I don't think it's any good.
for(i = 0; i < image1->nx; i++)
{
for(j = 0; j < image1->ny; j++)
{
if(i == 0 || j == 0 || i == image1->nx - 1 || j == image1->ny - 1)
{
image2->image[i][j] = image1->image[i][j];
}
else
{
int average = 0;
average += image1->image[i][j];
average += image1->image[i+1][j];
average += image1->image[i][j+1];
average += image1->image[i+1][j+1];
average += image1->image[i-1][j];
average += image1->image[i-1][j+1];
average += image1->image[i-1][j-1];
average += image1->image[i][j-1];
average += image1->image[i+1][j-1];
average /= 9;
image2->image[i][j] = average;
}
}
}
My struct in C is something like this:
struct pgm_image
{
int nx; // row number
int ny; // cell number
unsigned char image[N1][N2]; // image information
};
This looks right. To shorten it a bit, I guess you could use a for loop.
int min = -1;
int max = 1;
int average = 0;
int amount = 0;
for(int k = min; k <= max; k++){
for(int l = min; l <= max; l++){
amount++;
average += image1->image[i+k][j+l];
}
}
average /= amount;
image2->image[i][j] = average;
It still looks messy, but this way you can alter the smooth "radius" by changing the min and max variables.