Exercise 3 for the course "Parallel and distributed systems" of THMMY in AUTH university.
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#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <float.h>
#include "serialDeclarations.h"
void meanshift(double **x, int h, struct parameters *opt){
double **y;
y = alloc_2d_double(ROWS, COLUMNS);
y = duplicate(x, y, ROWS, COLUMNS);
// mean shift vectors
double **m;
m = alloc_2d_double(ROWS, COLUMNS);
// initialize elements of m to inf
for (int i=0;i<ROWS;i++){
for (int j=0;j<COLUMNS;j++){
m[i][j] = DBL_MAX;
}
}
// initialize iteration counter
int iter = 0;
// printf("%f \n", opt->epsilom);
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/** iterate until convergence **/
// printf("norm : %f \n", norm(m, ROWS, COLUMNS));
while (norm(m, ROWS, COLUMNS) > opt->epsilom) {
iter = iter +1;
// find pairwise distance matrix (inside radius)
/** allocate memory for inside iteration arrays **/
double ** W = alloc_2d_double(ROWS, ROWS);
double * l = malloc(ROWS * sizeof(double));
// [I, D] = rangesearch(x,y,h);
for (int i=0; i<ROWS; i++){
for (int j=0; j<ROWS; j++){
double dist = calculateDistance(y[i],x[j]);
// 2sparse matrix
if (dist < h){
W[i][j] = dist;
//printf("%f \n", W[i][j]);
}else{
W[i][j] = 0;
}
}
}
// for each element of W (x) do x^2
// size of W is [600 600]
// W is a sparse matrix -> apply to non-zero elements
for (int i=0; i<ROWS; i++){
double sum =0;
for (int j=0; j < ROWS; j++){
if (W[i][j] != 0){
W[i][j] = W[i][j]*W[i][j];
// compute kernel matrix
// apply function to non zero elements of a sparse matrix
double pow = ((-1)*(W[i][j]))/(2*(h*h));
W[i][j] = exp(pow);
}
// make sure diagonal elements are 1
if (i==j){
W[i][j] = W[i][j] +1;
}
// calculate sum(W,2)
sum = sum + W[i][j];
}
/** l array is correct**/
l[i] = sum;
// printf("l[%d] : %f \n", i, l[i]);
}
/** W is correct**/
//print_matrix(W, ROWS, ROWS);
// create new y vector
double** y_new = alloc_2d_double(ROWS, COLUMNS);
multiply(W, x, y_new);
/** y_new is CORRECT **/
// print_matrix(y_new, ROWS, COLUMNS);
// divide element-wise
for (int i=0; i<ROWS; i++){
for (int j=0; j<COLUMNS; j++){
y_new[i][j] = y_new[i][j] / l[i];
}
}
// calculate mean-shift vector
for (int i=0; i<ROWS; i++){
for (int j=0; j<COLUMNS; j++){
m[i][j] = y_new[i][j] - y[i][j];
// update y
y[i][j] = y_new[i][j];
}
}
printf("Iteration n. %d, error %f \n", iter, norm(m, ROWS, COLUMNS));
// TODO maybe keep y for live display later?
};
}
// allocates a 2d array in continuous memory positions
double **alloc_2d_double(int rows, int cols) {
double *data = (double *)malloc(rows*cols*sizeof(double));
double **array= (double **)malloc(rows*sizeof(double*));
for (int i=0; i<rows; i++)
array[i] = &(data[cols*i]);
return array;
}
// copy the values of a 2d double array to another
double **duplicate(double **a, double **b, int rows, int cols){
for (int i=0;i<rows;i++){
for (int j=0;j<cols;j++){
b[i][j] = a[i][j];
}
}
return b;
}
// TODO check why there's is a difference in the norm calculate in matlab
double norm(double ** m, int rows, int cols){
double sum=0, a=0;
for (int i = 0; i < rows; i++) {
for (int j = 0; j < cols; j++) {
a = m[i][j] * m[i][j];
sum = sum + a;
}
}
double norm = sqrt(sum);
return norm;
}
double calculateDistance(double *y, double *x){
double sum = 0, dif;
for (int i=0;i<COLUMNS;i++){
dif = y[i]-x[i];
sum += dif * dif;
}
double distance = sqrt(sum);
return distance;
}
void multiply(double ** matrix1, double ** matrix2, double ** output){
// W dims are ROWS ROWS and x dims are ROWS COLUMNS
7 years ago
int i, j, k;
for (i=0; i<ROWS; i++){
for (j=0; j<COLUMNS; j++){
output[i][j] = 0;
for (k=0; k<ROWS; k++){
output[i][j] += matrix1[i][k] * matrix2[k][j];
}
}
}
}
void print_matrix(double ** array, int rows, int cols){
for (int i=0; i<cols; i++){
for (int j=0; j<rows; j++){
printf("%f ", array[j][i]);
}
printf("\n");
}
}