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