void example_kmeans( int nrows, int ncols,
double** data,
int nclusters, int npass,
char dist, char* jobname)
/* Perform k-means clustering on genes */
{
int i, j, ii, nl, nc;
//const int nclusters = 3;
const int transpose = 0;
//const char dist = 'e';
const char method = 'a';
/* For method=='a', the centroid is defined as the mean over all elements
belonging to a cluster for each dimension.
For method=='m', the centroid is defined as the median over all elements
belonging to a cluster for each dimension.
*/
//int npass = 1;
int ifound = 0;
int test=0;
double error;
double distance;
int** index;
int* count;
double* weight = malloc(ncols*sizeof(double));
int* clusterid = malloc(nrows*sizeof(int));
double** cdata = malloc(nclusters*sizeof(double*));
int n=0;
char* filename;
char* filename2;
char* filename3;
FILE *out1=NULL;
FILE *out2=NULL;
FILE *out3=NULL;
for (i = 0; i < nclusters; i++)
{ cdata[i] = malloc(ncols*sizeof(double));
}
for (i = 0; i < ncols; i++) weight[i] = 1.0;
n = 1 + strlen(jobname) + strlen("_K_G") + strlen(".ext");
if (dist)
{ int dummy = nclusters;
do n++; while (dummy/=10);
}
//avovk
printf("a je u omari :) \n");
filename = malloc(n*sizeof(char));
filename2 = malloc(n*sizeof(char));
filename3 = malloc(n*sizeof(char));
sprintf (filename, "%s_K_G%d.kgg", jobname, nclusters);
out1 = fopen( filename, "w" );
sprintf (filename2, "%s_K_G%d.dis", jobname, nclusters);
out2 = fopen( filename2, "w" );
sprintf (filename3, "%s_K_G%d.cen", jobname, nclusters);
out3 = fopen( filename3, "w" );
printf("======================== k-means clustering"
" ========================\n");
printf ("\n");
printf ("----- doing %d passes... go stretch your legs...\n",npass);
//npass = 3;
kcluster(nclusters,nrows,ncols,data,weight,transpose,npass,method,dist,
clusterid, &error, &ifound);
printf ("Solution found %d times; ", ifound);
printf ("within-cluster sum of distances is %f\n", error);
printf ("------- writing Cluster assignments to file:\t\t"
" %s_K_G%d.kgg\n",jobname, nclusters);
for (i = 0; i < nrows; i++)
fprintf (out1, "%09d\t %d\n", i, clusterid[i]);
fclose(out1); out1=NULL;
printf ("------- writing Distance between clusters to file:\t %s_K_G%d.dis \n", jobname, nclusters);
fprintf (out2,"------- Distance between clusters:\n");
index = malloc(nclusters*sizeof(int*));
count = malloc(nclusters*sizeof(int));
for (i = 0; i < nclusters; i++) count[i] = 0;
for (i = 0; i < nrows; i++) count[clusterid[i]]++;
for (i = 0; i < nclusters; i++) index[i] = malloc(count[i]*sizeof(int));
for (i = 0; i < nclusters; i++) count[i] = 0;
for (i = 0; i < nrows; i++)
{ int id = clusterid[i];
index[id][count[id]] = i;
count[id]++;
}
for (i = 0; i < nclusters-1; i++)
{
for (j = 1+i; j < nclusters; j++)
{
distance = clusterdistance(nrows, ncols, data, weight, count[i], count[j], index[i], index[j], 'e', 'a', 0);
fprintf(out2,"Distance between %d and %d: %7.3f\n", i, j, distance);
// fprintf(stderr,"Distance between %d and %d: %7.3f\n", i, j, distance);
}
}
fclose(out2); out2=NULL;
printf ("------- writing Cluster centroids to file:\t\t%s_K_G%d.cen\n",jobname, nclusters);
fprintf (out3,"------- Cluster centroids:\n");
getclustercentroids(nclusters, nrows, ncols, data, clusterid, cdata, 0, 'a');
fprintf(out3," coefficients:");
for(i=0; i<ncols; i++) fprintf(out3,"\t%7d", i);
fprintf(out3,"\n");
for (i = 0; i < nclusters; i++){
fprintf(out3,"Cluster %2d:", i);
for (j = 0; j < ncols; j++) fprintf(out3,"\t%7.3f", cdata[i][j]);
fprintf(out3,"\n");
}
fclose(out3); out3=NULL;
printf("Done...\n");
/* call function to calculate distance between each voxel and centroid */
/* we will need:
count - number of elements in cluster as we allready have it
cdata - cluster centroids
clusterid
data */
getvoxlclusterdist(count, cdata, clusterid, data, jobname,
nclusters, nrows, ncols);
for (i = 0; i < nclusters; i++) free(index[i]);
free(index);
free(count);
for (i = 0; i < nclusters; i++){
free(cdata[i]);
}
free(cdata);
free(clusterid);
free(weight);
return;
}
void example_kmeans( int nrows, int ncols,
double** data,
int nclusters, int npass,
char dist, char* jobname,
int *clusterid)
/* Perform k-means clustering on genes */
{
int i, j, ii, nl, nc;
//const int nclusters = 3;
const int transpose = 0;
//const char dist = 'e';
const char method = 'a';
/* For method=='a', the centroid is defined as the mean over all elements
belonging to a cluster for each dimension.
For method=='m', the centroid is defined as the median over all elements
belonging to a cluster for each dimension.
*/
//int npass = 1;
int ifound = 0;
int test=0;
double error;
double distance;
int** index;
int* count;
double* weight = malloc(ncols*sizeof(double));
double** cdata = malloc(nclusters*sizeof(double*));
int** cmask = malloc(nclusters*sizeof(int*));
int** mask = NULL;
int n=0;
char* filename;
char* filename2;
char* filename3;
FILE *out1=NULL;
FILE *out2=NULL;
FILE *out3=NULL;
for (i = 0; i < nclusters; i++)
{ cdata[i] = malloc(ncols*sizeof(double));
cmask[i] = malloc(ncols*sizeof(int));
}
for (i = 0; i < ncols; i++) weight[i] = 1.0;
mask = (int **)calloc(sizeof(int*), nrows);
for (ii=0;ii<nrows;++ii) {
mask[ii] = (int *)calloc(sizeof(int),ncols);
}
for (nl=0; nl<nrows; ++nl) {
for (nc=0; nc<ncols; ++nc) {
mask[nl][nc] = 1;
}
}
n = 1 + strlen(jobname) + strlen("_K_G") + strlen(".ext");
if (dist)
{ int dummy = nclusters;
do n++; while (dummy/=10);
}
//avovk
printf("a je u omari :) \n");
filename = (char *)malloc(n*sizeof(char));
filename2 = (char *)malloc(n*sizeof(char));
filename3 = (char *)malloc(n*sizeof(char));
sprintf (filename, "%s_K_G%d.kgg", jobname, nclusters);
out1 = fopen( filename, "w" );
sprintf (filename2, "%s_K_G%d.dis", jobname, nclusters);
out2 = fopen( filename2, "w" );
sprintf (filename3, "%s_K_G%d.cen", jobname, nclusters);
out3 = fopen( filename3, "w" );
printf("======================== k-means clustering"
" ========================\n");
printf ("\n");
printf ("----- doing %d passes... go stretch your legs...\n",npass);
//npass = 3;
/* ANDREJ: This function returns different answers each time it is
executed. Does the library provide for ways to initialize the
random number generators used for the searching and initializations? */
kcluster( nclusters,
nrows,ncols,data,
mask,weight,
transpose,npass,
method,dist,
clusterid, &error, &ifound);
printf ("Solution found %d times; ", ifound);
printf ("within-cluster sum of distances is %f\n", error);
printf ("------- writing Cluster assignments to file:\t\t"
" %s_K_G%d.kgg\n",jobname, nclusters);
for (i = 0; i < nrows; i++)
fprintf (out1, "%09d\t %d\n", i, clusterid[i]);
fclose(out1); out1=NULL;
printf ("------- writing Distance between clusters to file:\t "
"%s_K_G%d.dis \n", jobname, nclusters);
fprintf (out2,"------- Distance between clusters:\n");
index = (int **)malloc(nclusters*sizeof(int*));
count = (int *)malloc(nclusters*sizeof(int));
for (i = 0; i < nclusters; i++) count[i] = 0;
for (i = 0; i < nrows; i++) count[clusterid[i]]++;
for (i = 0; i < nclusters; i++) index[i] = malloc(count[i]*sizeof(int));
for (i = 0; i < nclusters; i++) count[i] = 0;
for (i = 0; i < nrows; i++){
int id = clusterid[i];
index[id][count[id]] = i;
count[id]++;
}
distance =
clusterdistance(nrows, ncols, data, mask, weight, count[0], count[1],
index[0], index[1], 'e', 'a', 0);
fprintf(out2,"Distance between 0 and 1: %7.3f\n", distance);
distance =
clusterdistance(nrows, ncols, data, mask, weight, count[0], count[2],
index[0], index[2], 'e', 'a', 0);
fprintf(out2,"Distance between 0 and 2: %7.3f\n", distance);
distance =
clusterdistance(nrows, ncols, data, mask, weight, count[1], count[2],
index[1], index[2], 'e', 'a', 0);
fprintf(out2,"Distance between 1 and 2: %7.3f\n", distance);
fclose(out2); out2=NULL;
printf ("------- writing Cluster centroids to file:\t\t"
"%s_K_G%d.cen\n",jobname, nclusters);
fprintf (out3,"------- Cluster centroids:\n");
getclustercentroids(nclusters, nrows, ncols, data, mask, clusterid,
cdata, cmask, 0, 'a');
fprintf(out3," coefficients:");
for(i=0; i<ncols; i++) fprintf(out3,"\t%7d", i);
fprintf(out3,"\n");
for (i = 0; i < nclusters; i++){
fprintf(out3,"Cluster %2d:", i);
for (j = 0; j < ncols; j++) fprintf(out3,"\t%7.3f", cdata[i][j]);
fprintf(out3,"\n");
}
fclose(out3); out3=NULL;
printf("Done...\n");
for (i = 0; i < nclusters; i++) free(index[i]);
free(index);
free(count);
for (i = 0; i < nclusters; i++){
free(cdata[i]);
free(cmask[i]);
}
for (ii=0;ii<nrows;++ii) {
if (mask[ii]) free(mask[ii]);
}
free(cdata);
free(cmask);
free(weight);
free(mask);
return;
}
void example_kmeans(int nrows, int ncols, double** data, int** mask)
/* Perform k-means clustering on genes */
{ int i, j;
const int nclusters = 3;
const int transpose = 0;
const char dist = 'e';
const char method = 'a';
int npass = 1;
int ifound = 0;
double error;
double distance;
int** index;
int* count;
double* weight = malloc(ncols*sizeof(double));
int* clusterid = malloc(nrows*sizeof(int));
double** cdata = malloc(nclusters*sizeof(double*));
int** cmask = malloc(nclusters*sizeof(int*));
for (i = 0; i < nclusters; i++)
{ cdata[i] = malloc(ncols*sizeof(double));
cmask[i] = malloc(ncols*sizeof(int));
}
for (i = 0; i < ncols; i++) weight[i] = 1.0;
printf("======================== k-means clustering ====================\n");
printf("\n");
printf("----- one pass of the EM algorithm (results may change)\n");
kcluster(nclusters,nrows,ncols,data,mask,weight,transpose,npass,method,dist,
clusterid, &error, &ifound);
printf ("Solution found %d times; within-cluster sum of distances is %f\n",
ifound, error);
printf ("Cluster assignments:\n");
for (i = 0; i < nrows; i++)
printf ("Gene %d: cluster %d\n", i, clusterid[i]);
printf ("\n");
printf("----- 1000 passes of the EM algorithm (result should not change)\n");
npass = 1000;
kcluster(nclusters,nrows,ncols,data,mask,weight,transpose,npass,method,dist,
clusterid, &error, &ifound);
printf ("Solution found %d times; ", ifound);
printf ("within-cluster sum of distances is %f\n", error);
printf ("Cluster assignments:\n");
for (i = 0; i < nrows; i++)
printf ("Gene %d: cluster %d\n", i, clusterid[i]);
printf ("\n");
printf ("------- Distance between clusters:\n");
index = malloc(nclusters*sizeof(int*));
count = malloc(nclusters*sizeof(int));
for (i = 0; i < nclusters; i++) count[i] = 0;
for (i = 0; i < nrows; i++) count[clusterid[i]]++;
for (i = 0; i < nclusters; i++) index[i] = malloc(count[i]*sizeof(int));
for (i = 0; i < nclusters; i++) count[i] = 0;
for (i = 0; i < nrows; i++)
{ int id = clusterid[i];
index[id][count[id]] = i;
count[id]++;
}
distance =
clusterdistance(nrows, ncols, data, mask, weight, count[0], count[1],
index[0], index[1], 'e', 'a', 0);
printf("Distance between 0 and 1: %7.3f\n", distance);
distance =
clusterdistance(nrows, ncols, data, mask, weight, count[0], count[2],
index[0], index[2], 'e', 'a', 0);
printf("Distance between 0 and 2: %7.3f\n", distance);
distance =
clusterdistance(nrows, ncols, data, mask, weight, count[1], count[2],
index[1], index[2], 'e', 'a', 0);
printf("Distance between 1 and 2: %7.3f\n", distance);
printf ("\n");
printf ("------- Cluster centroids:\n");
getclustermean(nclusters, nrows, ncols, data, mask, clusterid,
cdata, cmask, 0);
printf(" Microarray:");
for(i=0; i<ncols; i++) printf("\t%7d", i);
printf("\n");
for (i = 0; i < nclusters; i++)
{ printf("Cluster %2d:", i);
for (j = 0; j < ncols; j++) printf("\t%7.3f", cdata[i][j]);
printf("\n");
}
printf("\n");
for (i = 0; i < nclusters; i++) free(index[i]);
free(index);
free(count);
for (i = 0; i < nclusters; i++)
{ free(cdata[i]);
free(cmask[i]);
}
free(cdata);
free(cmask);
free(clusterid);
free(weight);
return;
}
