remove unused SparseMatrix_k_centers

diff --git a/lib/sparse/SparseMatrix.c b/lib/sparse/SparseMatrix.c
index ad859df2e07fcebff9b06bb3b685f8235b0ef130..59241a642cbbf434cae30dff05ee33a0ca5ea608 100644 (file)
--- a/lib/sparse/SparseMatrix.c
+++ b/lib/sparse/SparseMatrix.c
@@ -2872,147 +2872,6 @@ SparseMatrix SparseMatrix_complement(SparseMatrix A, int undirected){
   return B;
 }
 
-int SparseMatrix_k_centers(SparseMatrix D0, int weighted, int K, int root, int **centers, int centering, real **dist0){
-  /*
-    Input:
-    D: the graph. If weighted, the entry values is used.
-    weighted: whether to treat the graph as weighted
-    K: the number of centers
-    root: the start node to find the k center.
-    centering: whether the distance should be centered so that sum_k dist[n*k+i] = 0
-    Output:
-    centers: the list of nodes that form the k-centers. If centers = NULL on input, it will be allocated.
-    dist: of dimension k*n, dist[k*n: (k+1)*n) gives the distance of every node to the k-th center.
-    return: flag. if not zero, the graph is not connected, or out of memory.
-  */
-  SparseMatrix D = D0;
-  int m = D->m, n = D->n;
-  int *levelset_ptr = NULL, *levelset = NULL, *mask = NULL;
-  int aggressive = FALSE;
-  int connectedQ, end1, end2;
-  enum {K_CENTER_DISCONNECTED = 1, K_CENTER_MEM};
-  real *dist_min = NULL, *dist_sum = NULL, dmax, dsum;
-  real *dist = NULL;
-  int nlist, *list = NULL;
-  int flag = 0, i, j, k, nlevel;
-  int check_connected = FALSE;
-
-  if (!SparseMatrix_is_symmetric(D, FALSE)){
-    D = SparseMatrix_symmetrize(D, FALSE);
-  }
-
-  assert(m == n);
-
-  dist_min = MALLOC(sizeof(real)*n);
-  dist_sum = MALLOC(sizeof(real)*n);
-  for (i = 0; i < n; i++) dist_min[i] = -1;
-  for (i = 0; i < n; i++) dist_sum[i] = 0;
-  if (!(*centers)) *centers = MALLOC(sizeof(int)*K);
-  if (!(*dist0)) *dist0 = MALLOC(sizeof(real)*K*n);
-  if (!weighted){
-    dist = MALLOC(sizeof(real)*n);
-    SparseMatrix_pseudo_diameter_unweighted(D, root, aggressive, &end1, &end2, &connectedQ);
-    if (check_connected && !connectedQ) {
-      flag =  K_CENTER_DISCONNECTED;
-      goto RETURN;
-    }
-    root = end1;
-    for (k = 0; k < K; k++){
-      (*centers)[k] = root;
-      //      fprintf(stderr,"k = %d, root = %d\n",k, root+1);
-      SparseMatrix_level_sets(D, root, &nlevel, &levelset_ptr, &levelset, &mask, TRUE);
-      if (check_connected) assert(levelset_ptr[nlevel] == n);
-      for (i = 0; i < nlevel; i++) {
-       for (j = levelset_ptr[i]; j < levelset_ptr[i+1]; j++){
-         (*dist0)[k*n+levelset[j]] = i;
-         if (k == 0){
-           dist_min[levelset[j]] = i;
-         } else {
-           dist_min[levelset[j]] = MIN(dist_min[levelset[j]], i);
-         }
-         dist_sum[levelset[j]] += i;
-       }
-      }
-
-      /* root = argmax_i min_roots dist(i, roots) */
-      dmax = dist_min[0];
-      dsum = dist_sum[0];
-      root = 0;
-      for (i = 0; i < n; i++) {
-       if (!check_connected && dist_min[i] < 0) continue;/* if the graph is disconnected, then we can not count on every node to be in level set. 
-                                                            Usee dist_min<0 to identify those not in level set */
-       if (dmax < dist_min[i] || (dmax == dist_min[i] && dsum < dist_sum[i])){/* tie break with avg dist */
-         dmax = dist_min[i];
-         dsum = dist_sum[i];
-         root = i;
-       }
-      }
-    }
- } else {
-    SparseMatrix_pseudo_diameter_weighted(D, root, aggressive, &end1, &end2, &connectedQ);
-    if (check_connected && !connectedQ) return K_CENTER_DISCONNECTED;
-    root = end1;
-    list = MALLOC(sizeof(int)*n);
-
-    for (k = 0; k < K; k++){
-      //fprintf(stderr,"k = %d, root = %d\n",k, root+1);
-      (*centers)[k] = root;
-      dist = &((*dist0)[k*n]);
-      flag = Dijkstra(D, root, dist, &nlist, list, &dmax);
-      if (flag){
-       flag = K_CENTER_MEM;
-       goto RETURN;
-      }
-      if (check_connected) assert(nlist == n);
-      for (i = 0; i < n; i++){
-       if (k == 0){
-         dist_min[i] = dist[i];
-       } else {
-         dist_min[i] = MIN(dist_min[i], dist[i]);
-       }
-       dist_sum[i] += dist[i];
-      }
-
-      /* root = argmax_i min_roots dist(i, roots) */
-      dmax = dist_min[0];
-      dsum = dist_sum[0];
-      root = 0;
-      for (i = 0; i < n; i++) {
-       if (!check_connected && dist_min[i] < 0) continue;/* if the graph is disconnected, then we can not count on every node to be in level set. 
-                                                            Usee dist_min<0 to identify those not in level set */
-       if (dmax < dist_min[i] || (dmax == dist_min[i] && dsum < dist_sum[i])){/* tie break with avg dist */
-         dmax = dist_min[i];
-         dsum = dist_sum[i];
-         root = i;
-       }
-      }
-    }
-    dist = NULL;
-  }
-
-  if (centering){
-    for (i = 0; i < n; i++) dist_sum[i] /= k;
-    for (k = 0; k < K; k++){
-      for (i = 0; i < n; i++){
-       (*dist0)[k*n+i] -= dist_sum[i];
-      }
-    }
-  }
-
- RETURN:
-  if (levelset_ptr) FREE(levelset_ptr);
-  if (levelset) FREE(levelset);
-  if (mask) FREE(mask);
-  
-  if (D != D0) SparseMatrix_delete(D);
-  if (dist) FREE(dist);
-  if (dist_min) FREE(dist_min);
-  if (dist_sum) FREE(dist_sum);
-  if (list) FREE(list);
-  return flag;
-
-}
-
 SparseMatrix SparseMatrix_from_dense(int m, int n, real *x){
   /* wrap a mxn matrix into a sparse matrix. the {i,j} entry of the matrix is in x[i*n+j], 0<=i<m; 0<=j<n */
   int i, j, *ja;

diff --git a/lib/sparse/SparseMatrix.h b/lib/sparse/SparseMatrix.h
index 0bdb1669a96a8dbd7d84c52cb161eea06371b105..9babf83d28e33826cb9f053dfe04a7a011408afb 100644 (file)
--- a/lib/sparse/SparseMatrix.h
+++ b/lib/sparse/SparseMatrix.h
@@ -112,9 +112,6 @@ SparseMatrix SparseMatrix_set_entries_to_real_one(SparseMatrix A);
 
 SparseMatrix SparseMatrix_complement(SparseMatrix A, int undirected);
 
-int SparseMatrix_k_centers(SparseMatrix D, int weighted, int K, int root, 
-                         int **centers, int centering, real **dist);
-
 int SparseMatrix_distance_matrix(SparseMatrix A, int weighted,  real **dist_matrix);
 SparseMatrix SparseMatrix_distance_matrix_khops(int khops, SparseMatrix A, int weighted);