src/avovk/Aclustering.c

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include "mrilib.h"
#include "cluster.h"


// from command.c file

static void display_help(void)
{ printf ("Clustering 4segmentation, command-line version.\n");
  printf ("USAGE: cluster [options]\n");
  printf ("options:\n");
  printf ("  -v, --version Version information\n");
  printf ("  -f filename   File loading\n");
  printf ("  -cg a|m       Specifies whether to center each row\n"
          "                in the data\n"
          "                a: Subtract the mean of each row\n"
          "                m: Subtract the median of each row\n"
          "                (default is no centering)\n");
  printf ("  -ng           Specifies to normalize each row in the data\n"
          "                (default is no normalization)\n");
  printf ("  -ca a|m       Specifies whether to center each column \n"
          "                in the data\n"
          "                a: Subtract the mean of each column\n"
          "                m: Subtract the median of each column\n"
          "                (default is no centering)\n");
  printf ("  -na           Specifies to normalize each column in the data\n"
          "                (default is no normalization)\n");
  printf ("  -u jobname    Allows you to specify a different name for the \n"
          "                output files.\n"
          "                (default is derived from the input file name)\n");
  printf ("  -g [0..8]     Specifies the distance measure for gene clustering \n"
         );
  printf ("                0: No gene clustering\n"
          "                1: Uncentered correlation\n"
          "                2: Pearson correlation\n"
          "                3: Uncentered correlation, absolute value\n"
          "                4: Pearson correlation, absolute value\n"
          "                5: Spearman's rank correlation\n"
          "                6: Kendall's tau\n"
          "                7: Euclidean distance\n"
          "                8: City-block distance\n"
          "                (default: 1)\n");
  printf ("  -k number     Specifies whether to run k-means clustering\n"
          "                instead of hierarchical clustering, and the number\n"
          "                of clusters k to use\n");
  printf ("  -c number     Specifies the number of clusters for tree cutting\n"
          "                after hierarchical clustering\n");
  printf ("  -r number     For k-means clustering, the number of times the\n"
          "                k-means clustering algorithm is run\n"
          "                (default: 1)\n");
  printf ("  -m [msca]     Specifies which hierarchical clustering method to use\n"
          "                m: Pairwise complete-linkage\n"
          "                s: Pairwise single-linkage\n"
          "                c: Pairwise centroid-linkage\n"
          "                a: Pairwise average-linkage\n"
          "                (default: m)\n");
  return;
}


static void display_version(void)
{ printf ("\n"
"Clustering for segmentation, command line version ,\n"
"using the C Clustering Library version 1.38.\n"
"\n"
"Cluster was originally written by Michael Eisen (eisen 'AT' rana.lbl.gov)\n"
"Copyright 1998-99 Stanford University.\n");
  printf ("\n"
"The command line version of Clustering version was created by Andrej Vovk\n"
"and will be surely corrected by Ziad Saad ;),\n"
"\n");
  return;
}

static char* setjobname(const char* basename, int strip)
{ char* jobname;
  int n = strlen(basename);
  if (strip)
  { char* extension = strrchr(basename, '.');
    if (extension) n -= strlen(extension);
  }
  jobname = malloc((n+1)*sizeof(char));
  strncpy (jobname, basename, n);
  jobname[n] = '\0';
  return jobname;
}

static int readnumber(const char word[])
{ char* error = 0;
  long value = 0;
  if (word[0] == '0' && word[1] != '\0') ++word;
  value = strtol(word,&error,0);
  if (*error=='\0') return (int)value;
  else return -1;
}

static char getmetric(int i)
{ switch (i)
  { case 1: return 'u';
    case 2: return 'c';
    case 3: return 'x';
    case 4: return 'a';
    case 5: return 's';
    case 6: return 'k';
    case 7: return 'e';
    case 8: return 'b';
    default: return '\0';
  }
  /* Never get here */
  return '\0';
}


/* ========================================================================= */


double** example_distance_gene(int nrows, int ncols, double** data)
/* Calculate the distance matrix between genes using the Euclidean distance. */
{ int i, j, ii, nl, nc;
  double** distMatrix;
  double* weight = malloc(ncols*sizeof(double));
  int** mask = NULL;

  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;
    }
  }


  printf("============ Euclidean distance matrix between genes ============\n");
  for (i = 0; i < ncols; i++) weight[i] = 1.0;
  distMatrix = distancematrix(nrows, ncols, data, mask, weight, 'e', 0); // ZIAD: SIGKILL
  if (!distMatrix)
  { printf ("Insufficient memory to store the distance matrix\n");
    free(weight);
    return NULL;
  }
  /*printf("   Gene:");
  for(i=0; i<nrows-1; i++) printf("%6d", i);
  printf("\n");
  for(i=0; i<nrows; i++)
     { printf("Gene %2d:",i);
    for(j=0; j<i; j++) printf(" %5.2f",distMatrix[i][j]);
    printf("\n");
  }
  printf("\n");
    */


  for (ii=0;ii<nrows;++ii) {
    if (mask[ii]) free(mask[ii]);
  }
  free(mask);
  free(weight);
  return distMatrix;
}


/* ========================================================================= */

void example_hierarchical(int nrows, int ncols, double** data, char* jobname, int k, double** distmatrix)
/* Perform hierarchical clustering ... , double** distmatrix */
{ int i, ii, nl, nc;
  const int nnodes = nrows-1;
  double* weight = malloc(ncols*sizeof(double));
  int* clusterid;
  Node* tree;
  int** mask = NULL;
  char* filename;
  //char* filename2;


  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;
    }
  }


  for (i = 0; i < ncols; i++) weight[i] = 1.0;
  printf("\n");


  FILE *out1;

  int n = 1 + strlen(jobname) + strlen("_C") + strlen(".ext");

  if (k)
    { int dummy = k;
      do n++; while (dummy/=10);
    }


  filename = malloc(n*sizeof(char));

  sprintf (filename, "%s_C%d.hie", jobname, k);
  out1 = fopen( filename, "w" );

  /*FILE *out2;
  filename2 = malloc(n*sizeof(char));

  sprintf (filename2, "%s_C%d.hi1", jobname, k);
  out2 = fopen( filename2, "w" );*/

  //HERE SHOULD USE method instead of 'xxx' (s,m,a,c)


  printf("================ Pairwise single linkage clustering ============\n");
  /* Since we have the distance matrix here, we may as well use it. */
  tree = treecluster(nrows, ncols, 0, 0, 0, 0, 'e', 's', distmatrix);
  /* The distance matrix was modified by treecluster, so we cannot use it any
   * more. But we still need to deallocate it here.
   * The first row of distmatrix is a single null pointer; no need to free it.
   */
  for (i = 1; i < nrows; i++) free(distmatrix[i]);
  free(distmatrix);
  if (!tree)
  { /* Indication that the treecluster routine failed */
    printf ("treecluster routine failed due to insufficient memory\n");
    free(weight);
    return;
  }

  #if 0
  /* Andrej: This block looked like it was commented out
    I took out some of the * / because they
    were generating warning and blocked out the
    entire section with #if 0 .  */

  /*fprintf(out2,"Node     Item 1   Item 2    Distance\n");
  for(i=0; i<nnodes; i++)
    fprintf(out2,"%3d:%9d%9d      %g\n",
           -i-1, tree[i].left, tree[i].right, tree[i].distance);
	   printf("\n");
	   fclose(out2);*/
  //free(tree);

  /*
  printf("================ Pairwise maximum linkage clustering ============\n");
  tree = treecluster(nrows, ncols, data, mask, weight, 0, 'e', 'm', 0); */
  /* Here, we let treecluster calculate the distance matrix for us. In that
   * case, the treecluster routine may fail due to insufficient memory to store
   * the distance matrix. For the small data sets in this example, that is
   * unlikely to occur though. Let's check for it anyway:
   */
  if (!tree)
  { /* Indication that the treecluster routine failed */
    printf ("treecluster routine failed due to insufficient memory\n");
    free(weight);
    return;
  }
  printf("Node     Item 1   Item 2    Distance\n");
  for(i=0; i<nnodes; i++)
    printf("%3d:%9d%9d      %g\n",
           -i-1, tree[i].left, tree[i].right, tree[i].distance);
  printf("\n");
  free(tree);


  printf("================ Pairwise average linkage clustering ============\n");
  tree = treecluster(nrows, ncols, data, mask, weight, 0, 'e', 'a', 0);
  if (!tree)
  { /* Indication that the treecluster routine failed */
    printf ("treecluster routine failed due to insufficient memory\n");
    free(weight);
    return;
  }
  printf("Node     Item 1   Item 2    Distance\n");
  for(i=0; i<nnodes; i++)
    printf("%3d:%9d%9d      %g\n",
           -i-1, tree[i].left, tree[i].right, tree[i].distance);
  printf("\n");
  free(tree);


  printf("================ Pairwise centroid linkage clustering ===========\n");
  tree = treecluster(nrows, ncols, data, mask, weight, 0, 'e', 'c', 0);
  if (!tree)
  { /* Indication that the treecluster routine failed */
    printf ("treecluster routine failed due to insufficient memory\n");
    free(weight);
    return;
  }
  printf("Node     Item 1   Item 2    Distance\n");
  for(i=0; i<nnodes; i++)
    printf("%3d:%9d%9d      %g\n",
           -i-1, tree[i].left, tree[i].right, tree[i].distance);
  printf("\n");


  #endif


  printf("=============== Cutting a hierarchical clustering tree ==========\n");
  clusterid = malloc(nrows*sizeof(int));
  printf(" number of clusters %d \n",k);
  cuttree (nrows, tree, k, clusterid);
  for(i=0; i<nrows; i++)
  fprintf(out1, "%09d\t%2d\n", i, clusterid[i]);
  fprintf(out1, "\n");
  fclose(out1);


  for (ii=0;ii<nrows;++ii) {
    if (mask[ii]) free(mask[ii]);
  }
  free(mask);
  free(tree);
  free(clusterid);
  free(weight);
  return;
}

void getvoxlclusterdist(int* count, double** cdata,
			int* clusterid, double** data, char* jobname,
			int nclusters, int nrows, int ncols)
{
  int i, j, n;
  char* filename4;
  FILE *out4=NULL;
  double* vcdata = malloc(nclusters*sizeof(double*));
  double difference, difference1;

  n = 1 + strlen(jobname) + strlen("_K_G") + strlen(".ext");

  int dummy = nclusters;
  do n++; while (dummy/=10);


  filename4 = malloc(n*sizeof(char));
  sprintf (filename4, "%s_K_G%d.vcd", jobname, nclusters);
  out4 = fopen( filename4, "w" );

  for (i = 0; i < nrows; i++){
    difference = 0;
    difference1 = 0;
    for (j = 0; j < ncols; j++) {
      difference1 = cdata[clusterid[i]][j]-data[i][j];
      difference = difference + difference1*difference1;
    }
    vcdata[i] = sqrt(difference);
  }

  printf ("------- writing Cluster assignments + distances from centroids to file:\t\t"
          " %s_K_G%d.vcd\n",jobname, nclusters);
  for (i = 0; i < nrows; i++)
    fprintf (out4, "%09d\t%d\t%7.3f\n", i, clusterid[i], vcdata[i]);
  fclose(out4); out4=NULL;

  /*for (i = 0; i < nrows; i++){
    free(vcdata[i]);
    }*/
  free(vcdata);
  return;

}


/* ========================================================================= */


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** 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 = 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,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 = 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, mask, 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, 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");

   /* 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(cmask[i]);
   }
   for (ii=0;ii<nrows;++ii) {
      if (mask[ii]) free(mask[ii]);
   }

   free(cdata);
   free(cmask);
   free(clusterid);
   free(weight);
   free(mask);
   return;
}


/* ========================================================================= */

int main(int argc, char **argv)
{
  int ii=0, ncol=0, nrow=0, nl=0, nc=0, posi=0, posj=0, posk=0;
  //int nclust=atoi(argv[2]);
  MRI_IMAGE *im = NULL;
  double *dar = NULL;
  double **D = NULL;
  //int **mask = NULL;
  double** distmatrix = NULL;
  //from command.c

  int i = 1;
  char* filename = 0;
  char* jobname = 0;
  int l = 0;
  int k = 0;
  int kh = 3;
  int r = 1;
  int s = 0;
  int x = 2;
  int y = 1;
  int Rows, Columns;
  char distmetric = 'u';
  char arraymetric = '\0';
  char method = 'm';
  char cg = '\0';
  char ca = '\0';
  int ng = 0;
  int na = 0;
  while (i < argc)
  { const char* const argument = argv[i];


    i++;
    if (strlen(argument)<2)
    { printf("ERROR: missing argument\n");
      return 0;
    }
    if (argument[0]!='-')
    { printf("ERROR: unknown argument\n");
      return 0;
    }
    if(!strcmp(argument,"--version") || !strcmp(argument,"-v"))
    { display_version();
      return 0;
    }
    if(!strcmp(argument,"--help") || !strcmp(argument,"-h"))
    { display_help();
      return 0;
    }
    if(!strcmp(argument,"-cg"))
    { if (i==argc || strlen(argv[i])>1 || !strchr("am",argv[i][0]))
      { printf ("Error reading command line argument cg\n");
        return 0;
      }
      cg = argv[i][0];
      i++;
      continue;
    }
    if(!strcmp(argument,"-ca"))
    { if (i==argc || strlen(argv[i])>1 || !strchr("am",argv[i][0]))
      { printf ("Error reading command line argument ca\n");
        return 0;
      }
      ca = argv[i][0];
      i++;
      continue;
    }
    if(!strcmp(argument,"-ng"))
    { ng = 1;
      continue;
    }
    if(!strcmp(argument,"-na"))
    { na = 1;
      continue;
    }
    switch (argument[1])
    { case 'l': l=1; break;
      case 'u':
      { if (i==argc)
        { printf ("Error reading command line argument u: "
                  "no job name specified\n");
          return 0;
        }
        jobname = setjobname(argv[i],0);
        i++;
        break;
      }
      case 'f':
      { if (i==argc)
        { printf ("Error reading command line argument f: "
                  "no file name specified\n");
          return 0;
        }
        filename = argv[i];
        i++;
        break;
      }
      case 'g':
      { int g;
        if (i==argc)
        { printf ("Error reading command line argument g: parameter missing\n");
          return 0;
        }
        g = readnumber(argv[i]);
        if (g < 0 || g > 9)
        { printf ("Error reading command line argument g: "
                  "should be between 0 and 9 inclusive\n");
          return 0;
        }
        i++;
        distmetric = getmetric(g);
        break;
      }

      case 'k':
      { if (i==argc)
        { printf ("Error reading command line argument k: "
                  "parameter missing\n");
          return 0;
        }
        k = readnumber(argv[i]);
        if (k < 1)
        { printf ("Error reading command line argument k: "
                  "a positive integer is required\n");
          return 0;
        }
        i++;
        break;
      }

    case 'c':
      { if (i==argc)
        { printf ("Error reading command line argument h: parameter missing\n");
          return 0;
        }
        kh = readnumber(argv[i]);
        if (kh < 1)
        { printf ("Error reading command line argument h: a positive integer is required\n");
          return 0;
        }
        i++;
        break;
      }
      case 'r':
      { if (i==argc)
        { printf ("Error reading command line argument r: parameter missing\n");
          return 0;
        }
        r = readnumber(argv[i]);
        if (r < 1)
        { printf ("Error reading command line argument r: "
                  "a positive integer is required\n");
          return 0;
        }
        i++;
        break;
      }
    case 'm':
      { if (i==argc || strlen(argv[i])>1 || !strchr("msca",argv[i][0]))
	  { printf ("Error reading command line argument m: should be 'm', 's', 'c', or 'a'\n");
	    return 0;
	  }
        method = argv[i][0];
        i++;
        break;
      }


      default: printf ("Unknown option\n");
    }
   }

   if(jobname==0) jobname = setjobname(filename,1);

   /*  else
   { display_help();
    return 0;
   }
   */

    if (argc < 2) {
      display_help();
      return 0;
    }

   //printf("num of clusters %d \n",nclust);
   fprintf(stderr,"Patience, reading %s...\n ", filename);
   im = mri_read_double_1D (filename);
   /* ZIAD I get this warning
   Aclustering.c:408: warning: passing argument 1 of mri_read_double_1D
                           discards qualifiers from pointer target type
   Andrej: filename was declared as (const char *),
          but the function expects (char *)              */
   if (!im) {
    fprintf(stderr,"Error: Failed to read matrix data from %s\n",
	    argv[2]);
    return(-1);
   }
   ncol = im->ny;
   nrow = im->nx;
   fprintf (stderr,"Have %d cols, %d rows\nNow...\n", ncol, nrow);

   /* now just get the array and kill the rest */
   dar = MRI_DOUBLE_PTR(im);

   /* make sure that pointer is set to NULL in im, or risk hell */
   mri_clear_data_pointer(im);
   if (im) mri_free(im); im = NULL; /* now kill im */


   /* for double loop*/
   D = (double **)calloc(sizeof(double*), nrow-1);

   for (ii=0;ii<(nrow-1);++ii) {
    D[ii] = (double *)calloc(sizeof(double), ncol-1);
   }

   for (nl=1; nl<nrow; ++nl) {
    for (nc=1; nc<ncol; ++nc) {
      D[nl-1][nc-1] = dar[nl+nc*nrow];
    }
    //fprintf(stdout,"\n");
  }

  //show_data(nrows, ncols, data, mask);
  //example_mean_median(nrows, ncols, data, mask);
  //distmatrix = example_distance_gene(nrows, ncols, data, mask);
  //if (distmatrix) example_hierarchical(nrows, ncols, data, mask, distmatrix);
  //example_distance_array(nrows, ncols, data, mask);
  if(k>0) example_kmeans(nrow-1, ncol-1, D, k, r, distmetric, jobname);

  else
    {
      distmatrix = example_distance_gene(nrow-1, ncol-1, D); // ZIAD: goes2 SIGKILL error
        if (distmatrix)
	  example_hierarchical(nrow-1, ncol-1, D, jobname, kh, distmatrix);
        }


   free(dar); dar = NULL; /* done with input array */
   // To free D
   for (ii=0;ii<(nrow-1);++ii) {

    if (D[ii]) free(D[ii]);
   }
   free(D);
   free(jobname);
   //free();

   fprintf (stderr,"\n");
   return 0;
   }

   /* How to compile:
   I copied Aclustering.c and cluster.c to /usr/local/bin/afni/src
   then compile with:
   gcc -Wall -Wno-unused Aclustering2.c cluster.c -o Aclustering -Inifti/niftilib -Inifti/nifticdf -Inifti/znzlib -L. -bind_at_load -l3DEdge -lmri -lf2c -lmri /usr/lib64/libXm.a -lXm -lXmu -lXp -lXpm -lXext -lXt -lX11 -lz -lexpat -lm -lc

   The output are 3 files
   - cluster_out.kgg -- as before, index number and cluster number
   - cluster_out.dis --
   - cluster_out.cen --


   actually
   cp /home/drejc/c_temp/Aclustering.c ./

   gcc -Wall -g -Wno-unused Aclustering.c cluster.c -o Aclustering -Inifti/niftilib -Inifti/nifticdf -Inifti/znzlib -L. -l3DEdge -lmri -lf2c -lmri /usr/lib64/libXm.a -lXm -lXmu -lXp -lXpm -lXext -lXt -lX11 -lz -lexpat -lm -lc

   cp Aclustering /home/drejc/segm4ziad/toandrej/out_mprage_10_smpl/

   */