void StressMajorizationSmoother_delete(StressMajorizationSmoother sm){
if (!sm) return;
if (sm->Lw) SparseMatrix_delete(sm->Lw);
if (sm->Lwd) SparseMatrix_delete(sm->Lwd);
if (sm->lambda) FREE(sm->lambda);
if (sm->data) sm->data_deallocator(sm->data);
FREE(sm);
}
void Multilevel_delete(Multilevel grid){
if (!grid) return;
if (grid->A){
if (grid->level == 0) {
if (grid->delete_top_level_A) SparseMatrix_delete(grid->A);
} else {
SparseMatrix_delete(grid->A);
}
}
SparseMatrix_delete(grid->P);
SparseMatrix_delete(grid->R);
if (grid->node_weights && grid->level > 0) FREE(grid->node_weights);
Multilevel_delete(grid->next);
FREE(grid);
}
void mq_clustering(SparseMatrix A, int inplace, int maxcluster, int use_value,
int *nclusters, int **assignment, real *mq, int *flag) {
/* find a clustering of vertices by maximize mq
A: symmetric square matrix n x n. If real value, value will be used as edges weights, otherwise edge weights are considered as 1.
inplace: whether A can e modified. If true, A will be modified by removing diagonal.
maxcluster: used to specify the maximum number of cluster desired, e.g., maxcluster=10 means that a maximum of 10 clusters
. is desired. this may not always be realized, and mq may be low when this is specified. Default: maxcluster = 0
nclusters: on output the number of clusters
assignment: dimension n. Node i is assigned to cluster "assignment[i]". 0 <= assignment < nclusters
*/
SparseMatrix B;
*flag = 0;
assert(A->m == A->n);
B = SparseMatrix_symmetrize(A, FALSE);
if (!inplace && B == A) {
B = SparseMatrix_copy(A);
}
B = SparseMatrix_remove_diagonal(B);
if (B->type != MATRIX_TYPE_REAL || !use_value) B = SparseMatrix_set_entries_to_real_one(B);
hierachical_mq_clustering(B, maxcluster, nclusters, assignment, mq, flag);
if (B != A) SparseMatrix_delete(B);
}
SparseMatrix call_tri(int n, int dim, real * x)
{
real one = 1;
int i, ii, jj;
SparseMatrix A;
SparseMatrix B;
int* edgelist = NULL;
real* xv = N_GNEW(n, real);
real* yv = N_GNEW(n, real);
int numberofedges;
for (i = 0; i < n; i++) {
xv[i] = x[i * 2];
yv[i] = x[i * 2 + 1];
}
if (n > 2) {
edgelist = delaunay_tri (xv, yv, n, &numberofedges);
} else {
numberofedges = 0;
}
A = SparseMatrix_new(n, n, 1, MATRIX_TYPE_REAL, FORMAT_COORD);
for (i = 0; i < numberofedges; i++) {
ii = edgelist[i * 2];
jj = edgelist[i * 2 + 1];
SparseMatrix_coordinate_form_add_entries(A, 1, &(ii), &(jj), &one);
}
if (n == 2) { /* if two points, add edge i->j */
ii = 0;
jj = 1;
SparseMatrix_coordinate_form_add_entries(A, 1, &(ii), &(jj), &one);
}
for (i = 0; i < n; i++) {
SparseMatrix_coordinate_form_add_entries(A, 1, &i, &i, &one);
}
B = SparseMatrix_from_coordinate_format(A);
SparseMatrix_delete(A);
A = SparseMatrix_symmetrize(B, FALSE);
SparseMatrix_delete(B);
B = A;
free (edgelist);
free (xv);
free (yv);
return B;
}
void Multilevel_MQ_Clustering_delete(Multilevel_MQ_Clustering grid) {
if (!grid) return;
if (grid->A) {
if (grid->level == 0) {
if (grid->delete_top_level_A) SparseMatrix_delete(grid->A);
} else {
SparseMatrix_delete(grid->A);
}
}
SparseMatrix_delete(grid->P);
SparseMatrix_delete(grid->R);
FREE(grid->matching);
FREE(grid->deg_intra);
FREE(grid->dout);
FREE(grid->wgt);
Multilevel_MQ_Clustering_delete(grid->next);
FREE(grid);
}
void uniform_stress(int dim, SparseMatrix A, real *x, int *flag){
UniformStressSmoother sm;
real lambda0 = 10.1, M = 100, scaling = 1.;
real res;
int maxit = 300, samepoint = TRUE, i, k, n = A->m;
SparseMatrix B = NULL;
*flag = 0;
/* just set random initial for now */
for (i = 0; i < dim*n; i++) {
x[i] = M*drand();
}
/* make sure x is not all at the same point */
for (i = 1; i < n; i++){
for (k = 0; k < dim; k++) {
if (ABS(x[0*dim+k] - x[i*dim+k]) > MACHINEACC){
samepoint = FALSE;
i = n;
break;
}
}
}
if (samepoint){
srand(1);
#ifdef DEBUG_PRINT
fprintf(stderr,"input coordinates to uniform_stress are the same, use random coordinates as initial input");
#endif
for (i = 0; i < dim*n; i++) x[i] = M*drand();
}
B = get_distance_matrix(A, scaling);
assert(SparseMatrix_is_symmetric(B, FALSE));
sm = UniformStressSmoother_new(dim, B, x, 1000000*lambda0, M, flag);
res = UniformStressSmoother_smooth(sm, dim, x, maxit);
UniformStressSmoother_delete(sm);
sm = UniformStressSmoother_new(dim, B, x, 10000*lambda0, M, flag);
res = UniformStressSmoother_smooth(sm, dim, x, maxit);
UniformStressSmoother_delete(sm);
sm = UniformStressSmoother_new(dim, B, x, 100*lambda0, M, flag);
res = UniformStressSmoother_smooth(sm, dim, x, maxit);
UniformStressSmoother_delete(sm);
sm = UniformStressSmoother_new(dim, B, x, lambda0, M, flag);
res = UniformStressSmoother_smooth(sm, dim, x, maxit);
UniformStressSmoother_delete(sm);
scale_to_box(0,0,7*70,10*70,A->m,dim,x);;
SparseMatrix_delete(B);
}
void stress_model_core(int dim, SparseMatrix B, real **x, int edge_len_weighted, int maxit_sm, real tol, int *flag){
int m;
SparseStressMajorizationSmoother sm;
real lambda = 0;
/*int maxit_sm = 1000, i; tol = 0.001*/
int i;
SparseMatrix A = B;
if (!SparseMatrix_is_symmetric(A, FALSE) || A->type != MATRIX_TYPE_REAL){
if (A->type == MATRIX_TYPE_REAL){
A = SparseMatrix_symmetrize(A, FALSE);
A = SparseMatrix_remove_diagonal(A);
} else {
A = SparseMatrix_get_real_adjacency_matrix_symmetrized(A);
}
}
A = SparseMatrix_remove_diagonal(A);
*flag = 0;
m = A->m;
if (!x) {
*x = MALLOC(sizeof(real)*m*dim);
srand(123);
for (i = 0; i < dim*m; i++) (*x)[i] = drand();
}
if (edge_len_weighted){
sm = SparseStressMajorizationSmoother_new(A, dim, lambda, *x, WEIGHTING_SCHEME_SQR_DIST, TRUE);/* do not under weight the long distances */
//sm = SparseStressMajorizationSmoother_new(A, dim, lambda, *x, WEIGHTING_SCHEME_INV_DIST, TRUE);/* do not under weight the long distances */
} else {
sm = SparseStressMajorizationSmoother_new(A, dim, lambda, *x, WEIGHTING_SCHEME_NONE, TRUE);/* weight the long distances */
}
if (!sm) {
*flag = -1;
goto RETURN;
}
sm->tol_cg = 0.1; /* we found that there is no need to solve the Laplacian accurately */
sm->scheme = SM_SCHEME_STRESS;
SparseStressMajorizationSmoother_smooth(sm, dim, *x, maxit_sm, tol);
for (i = 0; i < dim*m; i++) {
(*x)[i] /= sm->scaling;
}
SparseStressMajorizationSmoother_delete(sm);
RETURN:
if (A != B) SparseMatrix_delete(A);
}
static void get_neighborhood_precision_recall(char *outfile, SparseMatrix A0, real *ideal_dist_matrix, real *dist_matrix){
SparseMatrix A = A0;
int i, j, k, n = A->m;
// int *ia, *ja;
int *g_order = NULL, *p_order = NULL;/* ordering using graph/physical distance */
real *gdist, *pdist, radius;
int np_neighbors;
int ng_neighbors; /*number of (graph theoretical) neighbors */
real node_dist;/* distance of a node to the center node */
real true_positive;
real recall;
FILE *fp;
fp = fopen(outfile,"w");
if (!SparseMatrix_is_symmetric(A, FALSE)){
A = SparseMatrix_symmetrize(A, FALSE);
}
// ia = A->ia;
// ja = A->ja;
for (k = 5; k <= 50; k+= 5){
recall = 0;
for (i = 0; i < n; i++){
gdist = &(ideal_dist_matrix[i*n]);
vector_ordering(n, gdist, &g_order, TRUE);
pdist = &(dist_matrix[i*n]);
vector_ordering(n, pdist, &p_order, TRUE);
ng_neighbors = MIN(n-1, k); /* set the number of closest neighbor in the graph space to consider, excluding the node itself */
np_neighbors = ng_neighbors;/* set the number of closest neighbor in the embedding to consider, excluding the node itself */
radius = pdist[p_order[np_neighbors]];
true_positive = 0;
for (j = 1; j <= ng_neighbors; j++){
node_dist = pdist[g_order[j]];/* the phisical distance for j-th closest node (in graph space) */
if (node_dist <= radius) true_positive++;
}
recall += true_positive/np_neighbors;
}
recall /= n;
fprintf(fp,"%d %f\n", k, recall);
}
fprintf(stderr,"wrote precision/recall in file %s\n", outfile);
fclose(fp);
if (A != A0) SparseMatrix_delete(A);
FREE(g_order); FREE(p_order);
}
void stress_model(int dim, SparseMatrix B, real **x, int maxit_sm, real tol, int *flag){
int m;
SparseStressMajorizationSmoother sm;
real lambda = 0;
/*int maxit_sm = 1000, i; tol = 0.001*/
int i;
SparseMatrix A = B;
if (!SparseMatrix_is_symmetric(A, FALSE) || A->type != MATRIX_TYPE_REAL){
if (A->type == MATRIX_TYPE_REAL){
A = SparseMatrix_symmetrize(A, FALSE);
A = SparseMatrix_remove_diagonal(A);
} else {
A = SparseMatrix_get_real_adjacency_matrix_symmetrized(A);
}
}
A = SparseMatrix_remove_diagonal(A);
*flag = 0;
m = A->m;
if (!x) {
*x = MALLOC(sizeof(real)*m*dim);
srand(123);
for (i = 0; i < dim*m; i++) (*x)[i] = drand();
}
sm = SparseStressMajorizationSmoother_new(A, dim, lambda, *x, WEIGHTING_SCHEME_NONE);/* do not under weight the long distances */
if (!sm) {
*flag = -1;
goto RETURN;
}
SparseStressMajorizationSmoother_smooth(sm, dim, *x, maxit_sm, 0.001);
for (i = 0; i < dim*m; i++) {
(*x)[i] /= sm->scaling;
}
SparseStressMajorizationSmoother_delete(sm);
RETURN:
if (A != B) SparseMatrix_delete(A);
}
static SparseMatrix check_compatibility(SparseMatrix A, int ne, pedge *edges, int compatibility_method, real tol){
/* go through the links and make sure edges are compatible */
SparseMatrix B, C;
int *ia, *ja, i, j, jj;
real start;
real dist;
B = SparseMatrix_new(1, 1, 1, MATRIX_TYPE_REAL, FORMAT_COORD);
ia = A->ia; ja = A->ja;
//SparseMatrix_print("A",A);
start = clock();
for (i = 0; i < ne; i++){
for (j = ia[i]; j < ia[i+1]; j++){
jj = ja[j];
if (i == jj) continue;
if (compatibility_method == COMPATIBILITY_DIST){
dist = edge_compatibility_full(edges[i], edges[jj]);
} else if (compatibility_method == COMPATIBILITY_FULL){
dist = edge_compatibility(edges[i], edges[jj]);
}
/*
fprintf(stderr,"edge %d = {",i);
pedge_print("", edges[i]);
fprintf(stderr,"edge %d = {",jj);
pedge_print("", edges[jj]);
fprintf(stderr,"compatibility=%f\n",dist);
*/
if (ABS(dist) > tol){
B = SparseMatrix_coordinate_form_add_entries(B, 1, &i, &jj, &dist);
B = SparseMatrix_coordinate_form_add_entries(B, 1, &jj, &i, &dist);
}
}
}
C = SparseMatrix_from_coordinate_format(B);
//SparseMatrix_print("C",C);
SparseMatrix_delete(B);
B = C;
if (Verbose > 1)
fprintf(stderr, "edge compatibilitu time = %f\n",((real) (clock() - start))/CLOCKS_PER_SEC);
return B;
}
SparseMatrix call_tri2(int n, int dim, real * xx)
{
real *x, *y;
v_data *delaunay;
int i, j;
SparseMatrix A;
SparseMatrix B;
real one = 1;
x = N_GNEW(n, real);
y = N_GNEW(n, real);
for (i = 0; i < n; i++) {
x[i] = xx[dim * i];
y[i] = xx[dim * i + 1];
}
delaunay = UG_graph(x, y, n, 0);
A = SparseMatrix_new(n, n, 1, MATRIX_TYPE_REAL, FORMAT_COORD);
for (i = 0; i < n; i++) {
for (j = 1; j < delaunay[i].nedges; j++) {
SparseMatrix_coordinate_form_add_entries(A, 1, &i,
&(delaunay[i].
edges[j]), &one);
}
}
for (i = 0; i < n; i++) {
SparseMatrix_coordinate_form_add_entries(A, 1, &i, &i, &one);
}
B = SparseMatrix_from_coordinate_format(A);
B = SparseMatrix_symmetrize(B, FALSE);
SparseMatrix_delete(A);
free (x);
free (y);
freeGraph (delaunay);
return B;
}
void Multilevel_coarsen(SparseMatrix A, SparseMatrix *cA, SparseMatrix D, SparseMatrix *cD, real *node_wgt, real **cnode_wgt,
SparseMatrix *P, SparseMatrix *R, Multilevel_control ctrl, int *coarsen_scheme_used){
SparseMatrix cA0 = A, cD0 = NULL, P0 = NULL, R0 = NULL, M;
real *cnode_wgt0 = NULL;
int nc = 0, n;
*P = NULL; *R = NULL; *cA = NULL; *cnode_wgt = NULL, *cD = NULL;
n = A->n;
do {/* this loop force a sufficient reduction */
node_wgt = cnode_wgt0;
Multilevel_coarsen_internal(A, &cA0, D, &cD0, node_wgt, &cnode_wgt0, &P0, &R0, ctrl, coarsen_scheme_used);
if (!cA0) return;
nc = cA0->n;
#ifdef DEBUG_PRINT
if (Verbose) fprintf(stderr,"nc=%d n = %d\n",nc,n);
#endif
if (*P){
assert(*R);
M = SparseMatrix_multiply(*P, P0);
SparseMatrix_delete(*P);
SparseMatrix_delete(P0);
*P = M;
M = SparseMatrix_multiply(R0, *R);
SparseMatrix_delete(*R);
SparseMatrix_delete(R0);
*R = M;
} else {
*P = P0;
*R = R0;
}
if (*cA) SparseMatrix_delete(*cA);
*cA = cA0;
if (*cD) SparseMatrix_delete(*cD);
*cD = cD0;
if (*cnode_wgt) FREE(*cnode_wgt);
*cnode_wgt = cnode_wgt0;
A = cA0;
D = cD0;
node_wgt = cnode_wgt0;
cnode_wgt0 = NULL;
} while (nc > ctrl->min_coarsen_factor*n && ctrl->coarsen_mode == COARSEN_MODE_FORCEFUL);
}
static void Multilevel_coarsen(SparseMatrix A, SparseMatrix *cA, real *node_wgt, real **cnode_wgt,
SparseMatrix *P, SparseMatrix *R, Multilevel_control ctrl, int *coarsen_scheme_used){
int *matching = NULL, nmatch, nc, nzc, n, i;
int *irn = NULL, *jcn = NULL, *ia = NULL, *ja = NULL;
real *val = NULL;
SparseMatrix B = NULL;
int *vset = NULL, nvset, ncov, j;
int *cluster, *clusterp, ncluster;
assert(A->m == A->n);
*cA = NULL;
*P = NULL;
*R = NULL;
n = A->m;
*coarsen_scheme_used = ctrl->coarsen_scheme;
switch (ctrl->coarsen_scheme){
case COARSEN_HYBRID:
#ifdef DEBUG_PRINT
if (Verbose)
fprintf(stderr, "hybrid scheme, try COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE_LEAVES_FIRST first\n");
#endif
*coarsen_scheme_used = ctrl->coarsen_scheme = COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE_LEAVES_FIRST;
Multilevel_coarsen(A, cA, node_wgt, cnode_wgt, P, R, ctrl, coarsen_scheme_used);
if (!(*cA)) {
#ifdef DEBUG_PRINT
if (Verbose)
fprintf(stderr, "switching to COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE_SUPERNODES_FIRST\n");
#endif
*coarsen_scheme_used = ctrl->coarsen_scheme = COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE_SUPERNODES_FIRST;
Multilevel_coarsen(A, cA, node_wgt, cnode_wgt, P, R, ctrl, coarsen_scheme_used);
}
if (!(*cA)) {
#ifdef DEBUG_PRINT
if (Verbose)
fprintf(stderr, "switching to COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_CLUSTER_PERNODE_LEAVES_FIRST\n");
#endif
*coarsen_scheme_used = ctrl->coarsen_scheme = COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_CLUSTER_PERNODE_LEAVES_FIRST;
Multilevel_coarsen(A, cA, node_wgt, cnode_wgt, P, R, ctrl, coarsen_scheme_used);
}
if (!(*cA)) {
#ifdef DEBUG_PRINT
if (Verbose)
fprintf(stderr, "switching to COARSEN_INDEPENDENT_VERTEX_SET\n");
#endif
*coarsen_scheme_used = ctrl->coarsen_scheme = COARSEN_INDEPENDENT_VERTEX_SET;
Multilevel_coarsen(A, cA, node_wgt, cnode_wgt, P, R, ctrl, coarsen_scheme_used);
}
if (!(*cA)) {
#ifdef DEBUG_PRINT
if (Verbose)
fprintf(stderr, "switching to COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE\n");
#endif
*coarsen_scheme_used = ctrl->coarsen_scheme = COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE;
Multilevel_coarsen(A, cA, node_wgt, cnode_wgt, P, R, ctrl, coarsen_scheme_used);
}
ctrl->coarsen_scheme = COARSEN_HYBRID;
break;
case COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE_SUPERNODES_FIRST:
case COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_CLUSTER_PERNODE_LEAVES_FIRST:
case COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE_LEAVES_FIRST:
if (ctrl->coarsen_scheme == COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE_LEAVES_FIRST) {
maximal_independent_edge_set_heavest_edge_pernode_leaves_first(A, ctrl->randomize, &cluster, &clusterp, &ncluster);
} else if (ctrl->coarsen_scheme == COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE_SUPERNODES_FIRST) {
maximal_independent_edge_set_heavest_edge_pernode_supernodes_first(A, ctrl->randomize, &cluster, &clusterp, &ncluster);
} else {
maximal_independent_edge_set_heavest_cluster_pernode_leaves_first(A, 4, ctrl->randomize, &cluster, &clusterp, &ncluster);
}
assert(ncluster <= n);
nc = ncluster;
if (nc > ctrl->min_coarsen_factor*n || nc < ctrl->minsize) {
#ifdef DEBUG_PRINT
if (Verbose)
fprintf(stderr, "nc = %d, nf = %d, minsz = %d, coarsen_factor = %f coarsening stops\n",nc, n, ctrl->minsize, ctrl->min_coarsen_factor);
#endif
goto RETURN;
}
irn = N_GNEW(n,int);
jcn = N_GNEW(n,int);
val = N_GNEW(n,real);
nzc = 0;
for (i = 0; i < ncluster; i++){
for (j = clusterp[i]; j < clusterp[i+1]; j++){
assert(clusterp[i+1] > clusterp[i]);
irn[nzc] = cluster[j];
jcn[nzc] = i;
val[nzc++] = 1.;
}
}
assert(nzc == n);
*P = SparseMatrix_from_coordinate_arrays(nzc, n, nc, irn, jcn, (void *) val, MATRIX_TYPE_REAL);
*R = SparseMatrix_transpose(*P);
B = SparseMatrix_multiply(*R, A);
if (!B) goto RETURN;
*cA = SparseMatrix_multiply(B, *P);
if (!*cA) goto RETURN;
SparseMatrix_multiply_vector(*R, node_wgt, cnode_wgt, FALSE);
*R = SparseMatrix_divide_row_by_degree(*R);
SparseMatrix_set_symmetric(*cA);
SparseMatrix_set_pattern_symmetric(*cA);
*cA = SparseMatrix_remove_diagonal(*cA);
break;
case COARSEN_INDEPENDENT_EDGE_SET:
maximal_independent_edge_set(A, ctrl->randomize, &matching, &nmatch);
case COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE:
if (ctrl->coarsen_scheme == COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE)
maximal_independent_edge_set_heavest_edge_pernode(A, ctrl->randomize, &matching, &nmatch);
case COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE_DEGREE_SCALED:
if (ctrl->coarsen_scheme == COARSEN_INDEPENDENT_EDGE_SET_HEAVEST_EDGE_PERNODE_DEGREE_SCALED)
maximal_independent_edge_set_heavest_edge_pernode_scaled(A, ctrl->randomize, &matching, &nmatch);
nc = nmatch;
if (nc > ctrl->min_coarsen_factor*n || nc < ctrl->minsize) {
#ifdef DEBUG_PRINT
if (Verbose)
fprintf(stderr, "nc = %d, nf = %d, minsz = %d, coarsen_factor = %f coarsening stops\n",nc, n, ctrl->minsize, ctrl->min_coarsen_factor);
#endif
goto RETURN;
}
irn = N_GNEW(n,int);
jcn = N_GNEW(n,int);
val = N_GNEW(n,real);
nzc = 0; nc = 0;
for (i = 0; i < n; i++){
if (matching[i] >= 0){
if (matching[i] == i){
irn[nzc] = i;
jcn[nzc] = nc;
val[nzc++] = 1.;
} else {
irn[nzc] = i;
jcn[nzc] = nc;
val[nzc++] = 1;
irn[nzc] = matching[i];
jcn[nzc] = nc;
val[nzc++] = 1;
matching[matching[i]] = -1;
}
nc++;
matching[i] = -1;
}
}
assert(nc == nmatch);
assert(nzc == n);
*P = SparseMatrix_from_coordinate_arrays(nzc, n, nc, irn, jcn, (void *) val, MATRIX_TYPE_REAL);
*R = SparseMatrix_transpose(*P);
B = SparseMatrix_multiply(*R, A);
if (!B) goto RETURN;
*cA = SparseMatrix_multiply(B, *P);
if (!*cA) goto RETURN;
SparseMatrix_multiply_vector(*R, node_wgt, cnode_wgt, FALSE);
*R = SparseMatrix_divide_row_by_degree(*R);
SparseMatrix_set_symmetric(*cA);
SparseMatrix_set_pattern_symmetric(*cA);
*cA = SparseMatrix_remove_diagonal(*cA);
break;
case COARSEN_INDEPENDENT_VERTEX_SET:
case COARSEN_INDEPENDENT_VERTEX_SET_RS:
if (ctrl->coarsen_scheme == COARSEN_INDEPENDENT_VERTEX_SET){
maximal_independent_vertex_set(A, ctrl->randomize, &vset, &nvset, &nzc);
} else {
maximal_independent_vertex_set_RS(A, ctrl->randomize, &vset, &nvset, &nzc);
}
ia = A->ia;
ja = A->ja;
nc = nvset;
if (nc > ctrl->min_coarsen_factor*n || nc < ctrl->minsize) {
#ifdef DEBUG_PRINT
if (Verbose)
fprintf(stderr, "nc = %d, nf = %d, minsz = %d, coarsen_factor = %f coarsening stops\n",nc, n, ctrl->minsize, ctrl->min_coarsen_factor);
#endif
goto RETURN;
}
irn = N_GNEW(nzc,int);
jcn = N_GNEW(nzc,int);
val = N_GNEW(nzc,real);
nzc = 0;
for (i = 0; i < n; i++){
if (vset[i] == MAX_IND_VTX_SET_F){
ncov = 0;
for (j = ia[i]; j < ia[i+1]; j++){
if (vset[ja[j]] >= MAX_IND_VTX_SET_C){
ncov++;
}
}
assert(ncov > 0);
for (j = ia[i]; j < ia[i+1]; j++){
if (vset[ja[j]] >= MAX_IND_VTX_SET_C){
irn[nzc] = i;
jcn[nzc] = vset[ja[j]];
val[nzc++] = 1./(double) ncov;
}
}
} else {
assert(vset[i] >= MAX_IND_VTX_SET_C);
irn[nzc] = i;
jcn[nzc] = vset[i];
val[nzc++] = 1.;
}
}
*P = SparseMatrix_from_coordinate_arrays(nzc, n, nc, irn, jcn, (void *) val, MATRIX_TYPE_REAL);
*R = SparseMatrix_transpose(*P);
B = SparseMatrix_multiply(*R, A);
if (!B) goto RETURN;
*cA = SparseMatrix_multiply(B, *P);
if (!*cA) goto RETURN;
SparseMatrix_multiply_vector(*R, node_wgt, cnode_wgt, FALSE);
SparseMatrix_set_symmetric(*cA);
SparseMatrix_set_pattern_symmetric(*cA);
*cA = SparseMatrix_remove_diagonal(*cA);
break;
default:
goto RETURN;
}
RETURN:
if (matching) FREE(matching);
if (vset) FREE(vset);
if (irn) FREE(irn);
if (jcn) FREE(jcn);
if (val) FREE(val);
if (B) SparseMatrix_delete(B);
}
OverlapSmoother OverlapSmoother_new(SparseMatrix A, int m,
int dim, real lambda0, real *x, real *width, int include_original_graph, int neighborhood_only,
real *max_overlap, real *min_overlap,
int edge_labeling_scheme, int n_constr_nodes, int *constr_nodes, SparseMatrix A_constr, int shrink
){
OverlapSmoother sm;
int i, j, k, *iw, *jw, jdiag;
SparseMatrix B;
real *lambda, *d, *w, diag_d, diag_w, dist;
assert((!A) || SparseMatrix_is_symmetric(A, FALSE));
sm = GNEW(struct OverlapSmoother_struct);
sm->scheme = SM_SCHEME_NORMAL;
if (constr_nodes && n_constr_nodes > 0 && edge_labeling_scheme != ELSCHEME_NONE){
sm->scheme = SM_SCHEME_NORMAL_ELABEL;
sm->data = relative_position_constraints_new(A_constr, edge_labeling_scheme, n_constr_nodes, constr_nodes);
sm->data_deallocator = relative_position_constraints_delete;
} else {
sm->data = NULL;
}
sm->tol_cg = 0.01;
sm->maxit_cg = sqrt((double) A->m);
lambda = sm->lambda = N_GNEW(m,real);
for (i = 0; i < m; i++) sm->lambda[i] = lambda0;
B= call_tri(m, dim, x);
if (!neighborhood_only){
SparseMatrix C, D;
C = get_overlap_graph(dim, m, x, width, 0);
D = SparseMatrix_add(B, C);
SparseMatrix_delete(B);
SparseMatrix_delete(C);
B = D;
}
if (include_original_graph){
sm->Lw = SparseMatrix_add(A, B);
SparseMatrix_delete(B);
} else {
sm->Lw = B;
}
sm->Lwd = SparseMatrix_copy(sm->Lw);
#ifdef DEBUG
{
FILE *fp;
fp = fopen("/tmp/111","w");
export_embedding(fp, dim, sm->Lwd, x, NULL);
fclose(fp);
}
#endif
if (!(sm->Lw) || !(sm->Lwd)) {
OverlapSmoother_delete(sm);
return NULL;
}
assert((sm->Lwd)->type == MATRIX_TYPE_REAL);
ideal_distance_avoid_overlap(dim, sm->Lwd, x, width, (real*) (sm->Lwd->a), max_overlap, min_overlap);
/* no overlap at all! */
if (*max_overlap < 1 && shrink){
real scale_sta = MIN(1, *max_overlap*1.0001), scale_sto = 1;
if (Verbose) fprintf(stderr," no overlap (overlap = %f), rescale to shrink\n", *max_overlap - 1);
scale_sta = overlap_scaling(dim, m, x, width, scale_sta, scale_sto, 0.0001, 15);
*max_overlap = 1;
goto RETURN;
}
iw = sm->Lw->ia; jw = sm->Lw->ja;
w = (real*) sm->Lw->a; d = (real*) sm->Lwd->a;
for (i = 0; i < m; i++){
diag_d = diag_w = 0;
jdiag = -1;
for (j = iw[i]; j < iw[i+1]; j++){
k = jw[j];
if (k == i){
jdiag = j;
continue;
}
if (d[j] > 0){/* those edges that needs expansion */
w[j] = -100/d[j]/d[j];
/*w[j] = 100/d[j]/d[j];*/
} else {/* those that needs shrinking is set to negative in ideal_distance_avoid_overlap */
/*w[j] = 1/d[j]/d[j];*/
w[j] = -1/d[j]/d[j];
d[j] = -d[j];
}
dist = d[j];
diag_w += w[j];
d[j] = w[j]*dist;
diag_d += d[j];
}
lambda[i] *= (-diag_w);/* alternatively don't do that then we have a constant penalty term scaled by lambda0 */
assert(jdiag >= 0);
w[jdiag] = -diag_w + lambda[i];
d[jdiag] = -diag_d;
}
RETURN:
return sm;
}
real overlap_scaling(int dim, int m, real *x, real *width, real scale_sta, real scale_sto, real epsilon, int maxiter){
/* do a bisection between scale_sta and scale_sto, up to maxiter iterations or till interval <= epsilon, to find the best scaling to avoid overlap
m: number of points
x: the coordinates
width: label size
scale_sta: starting bracket. If <= 0, assumed 0. If > 0, we will test this first and if no overlap, return.
scale_sto: stopping bracket. This must be overlap free if positive. If <= 0, we will find automatically by doubling from scale_sta, or epsilon if scale_sta <= 0.
typically usage:
- for shrinking down a layout to reduce white space, we will assume scale_sta and scale_sto are both given and positive, and scale_sta is the current guess.
- for scaling up, we assume scale_sta, scale_sto <= 0
*/
real scale = -1, scale_best = -1;
SparseMatrix C = NULL;
int check_overlap_only = 1;
int overlap = 0;
real two = 2;
int iter = 0;
assert(epsilon > 0);
if (scale_sta <= 0) {
scale_sta = 0;
} else {
scale_coord(dim, m, x, scale_sta);
C = get_overlap_graph(dim, m, x, width, check_overlap_only);
if (!C || C->nz == 0) {
if (Verbose) fprintf(stderr," shrinking with with %f works\n", scale_sta);
SparseMatrix_delete(C);
return scale_sta;
}
scale_coord(dim, m, x, 1./scale_sta);
SparseMatrix_delete(C);
}
if (scale_sto < 0){
if (scale_sta == 0) {
scale_sto = epsilon;
} else {
scale_sto = scale_sta;
}
scale_coord(dim, m, x, scale_sto);
do {
scale_sto *= two;
scale_coord(dim, m, x, two);
C = get_overlap_graph(dim, m, x, width, check_overlap_only);
overlap = (C && C->nz > 0);
SparseMatrix_delete(C);
} while (overlap);
scale_coord(dim, m, x, 1/scale_sto);/* unscale */
}
scale_best = scale_sto;
while (iter++ < maxiter && scale_sto - scale_sta > epsilon){
fprintf(stderr,"in overlap_scaling iter=%d, maxiter=%d, scaling bracket: {%f,%f}\n", iter, maxiter, scale_sta, scale_sto);
scale = 0.5*(scale_sta + scale_sto);
scale_coord(dim, m, x, scale);
C = get_overlap_graph(dim, m, x, width, check_overlap_only);
scale_coord(dim, m, x, 1./scale);/* unscale */
overlap = (C && C->nz > 0);
SparseMatrix_delete(C);
if (overlap){
scale_sta = scale;
} else {
scale_best = scale_sto = scale;
}
}
/* final scaling */
scale_coord(dim, m, x, scale_best);
return scale_best;
}
static SparseMatrix get_overlap_graph(int dim, int n, real *x, real *width, int check_overlap_only){
/* if check_overlap_only = TRUE, we only check whether there is one overlap */
scan_point *scanpointsx, *scanpointsy;
int i, k, neighbor;
SparseMatrix A = NULL, B = NULL;
rb_red_blk_node *newNode, *newNode0, *newNode2 = NULL;
rb_red_blk_tree* treey;
real one = 1;
A = SparseMatrix_new(n, n, 1, MATRIX_TYPE_REAL, FORMAT_COORD);
scanpointsx = N_GNEW(2*n,scan_point);
for (i = 0; i < n; i++){
scanpointsx[2*i].node = i;
scanpointsx[2*i].x = x[i*dim] - width[i*dim];
scanpointsx[2*i].status = INTV_OPEN;
scanpointsx[2*i+1].node = i+n;
scanpointsx[2*i+1].x = x[i*dim] + width[i*dim];
scanpointsx[2*i+1].status = INTV_CLOSE;
}
qsort(scanpointsx, 2*n, sizeof(scan_point), comp_scan_points);
scanpointsy = N_GNEW(2*n,scan_point);
for (i = 0; i < n; i++){
scanpointsy[i].node = i;
scanpointsy[i].x = x[i*dim+1] - width[i*dim+1];
scanpointsy[i].status = INTV_OPEN;
scanpointsy[i+n].node = i;
scanpointsy[i+n].x = x[i*dim+1] + width[i*dim+1];
scanpointsy[i+n].status = INTV_CLOSE;
}
treey = RBTreeCreate(NodeComp,NodeDest,InfoDest,NodePrint,InfoPrint);
for (i = 0; i < 2*n; i++){
#ifdef DEBUG_RBTREE
fprintf(stderr," k = %d node = %d x====%f\n",(scanpointsx[i].node)%n, (scanpointsx[i].node), (scanpointsx[i].x));
#endif
k = (scanpointsx[i].node)%n;
if (scanpointsx[i].status == INTV_OPEN){
#ifdef DEBUG_RBTREE
fprintf(stderr, "inserting...");
treey->PrintKey(&(scanpointsy[k]));
#endif
RBTreeInsert(treey, &(scanpointsy[k]), NULL); /* add both open and close int for y */
#ifdef DEBUG_RBTREE
fprintf(stderr, "inserting2...");
treey->PrintKey(&(scanpointsy[k+n]));
#endif
RBTreeInsert(treey, &(scanpointsy[k+n]), NULL);
} else {
real bsta, bbsta, bsto, bbsto; int ii;
assert(scanpointsx[i].node >= n);
newNode = newNode0 = RBExactQuery(treey, &(scanpointsy[k + n]));
ii = ((scan_point *)newNode->key)->node;
assert(ii < n);
bsta = scanpointsy[ii].x; bsto = scanpointsy[ii+n].x;
#ifdef DEBUG_RBTREE
fprintf(stderr, "poping..%d....yinterval={%f,%f}\n", scanpointsy[k + n].node, bsta, bsto);
treey->PrintKey(newNode->key);
#endif
assert(treey->nil != newNode);
while ((newNode) && ((newNode = TreePredecessor(treey, newNode)) != treey->nil)){
neighbor = (((scan_point *)newNode->key)->node)%n;
bbsta = scanpointsy[neighbor].x; bbsto = scanpointsy[neighbor+n].x;/* the y-interval of the node that has one end of the interval lower than the top of the leaving interval (bsto) */
#ifdef DEBUG_RBTREE
fprintf(stderr," predecessor is node %d y = %f\n", ((scan_point *)newNode->key)->node, ((scan_point *)newNode->key)->x);
#endif
if (neighbor != k){
if (ABS(0.5*(bsta+bsto) - 0.5*(bbsta+bbsto)) < 0.5*(bsto-bsta) + 0.5*(bbsto-bbsta)){/* if the distance of the centers of the interval is less than sum of width, we have overlap */
A = SparseMatrix_coordinate_form_add_entries(A, 1, &neighbor, &k, &one);
#ifdef DEBUG_RBTREE
fprintf(stderr,"====================================== %d %d\n",k,neighbor);
#endif
if (check_overlap_only) goto check_overlap_RETURN;
}
} else {
newNode2 = newNode;
}
}
#ifdef DEBUG_RBTREE
fprintf(stderr, "deleteing...");
treey->PrintKey(newNode0->key);
#endif
if (newNode0) RBDelete(treey,newNode0);
if (newNode2 && newNode2 != treey->nil && newNode2 != newNode0) {
#ifdef DEBUG_RBTREE
fprintf(stderr, "deleteing2...");
treey->PrintKey(newNode2->key);
#endif
if (newNode0) RBDelete(treey,newNode2);
}
}
}
check_overlap_RETURN:
FREE(scanpointsx);
FREE(scanpointsy);
RBTreeDestroy(treey);
B = SparseMatrix_from_coordinate_format(A);
SparseMatrix_delete(A);
A = SparseMatrix_symmetrize(B, FALSE);
SparseMatrix_delete(B);
if (Verbose) fprintf(stderr, "found %d clashes\n", A->nz);
return A;
}
static void sfdpLayout(graph_t * g, spring_electrical_control ctrl,
int hops, pointf pad)
{
real *sizes;
real *pos;
Agnode_t *n;
int flag, i;
int n_edge_label_nodes = 0, *edge_label_nodes = NULL;
SparseMatrix D = NULL;
SparseMatrix A;
if (ctrl->method == METHOD_SPRING_MAXENT) /* maxent can work with distance matrix */
A = makeMatrix(g, Ndim, &D);
else
A = makeMatrix(g, Ndim, NULL);
if (ctrl->overlap >= 0) {
if (ctrl->edge_labeling_scheme > 0)
sizes = getSizes(g, pad, &n_edge_label_nodes, &edge_label_nodes);
else
sizes = getSizes(g, pad, NULL, NULL);
}
else
sizes = NULL;
pos = getPos(g, ctrl);
switch (ctrl->method) {
case METHOD_SPRING_ELECTRICAL:
case METHOD_SPRING_MAXENT:
multilevel_spring_electrical_embedding(Ndim, A, D, ctrl, NULL, sizes, pos, n_edge_label_nodes, edge_label_nodes, &flag);
break;
case METHOD_UNIFORM_STRESS:
uniform_stress(Ndim, A, pos, &flag);
break;
case METHOD_STRESS:{
int maxit = 200;
real tol = 0.001;
int weighted = TRUE;
if (!D){
D = SparseMatrix_get_real_adjacency_matrix_symmetrized(A);/* all distance 1 */
weighted = FALSE;
} else {
D = SparseMatrix_symmetrize_nodiag(D, FALSE);
weighted = TRUE;
}
if (hops > 0){
SparseMatrix DD;
DD = SparseMatrix_distance_matrix_khops(hops, D, weighted);
if (Verbose){
fprintf(stderr,"extracted a %d-neighborhood graph of %d edges from a graph of %d edges\n",
hops, (DD->nz)/2, (D->nz/2));
}
SparseMatrix_delete(D);
D = DD;
}
stress_model(Ndim, A, D, &pos, TRUE, maxit, tol, &flag);
}
break;
}
for (n = agfstnode(g); n; n = agnxtnode(g, n)) {
real *npos = pos + (Ndim * ND_id(n));
for (i = 0; i < Ndim; i++) {
ND_pos(n)[i] = npos[i];
}
}
free(sizes);
free(pos);
SparseMatrix_delete (A);
if (D) SparseMatrix_delete (D);
if (edge_label_nodes) FREE(edge_label_nodes);
}
real StressMajorizationSmoother_smooth(StressMajorizationSmoother sm, int dim, real *x, int maxit_sm, real tol) {
SparseMatrix Lw = sm->Lw, Lwd = sm->Lwd, Lwdd = NULL;
int i, j, k, m, *id, *jd, *iw, *jw, idiag, flag = 0, iter = 0;
real *w, *dd, *d, *y = NULL, *x0 = NULL, *x00 = NULL, diag, diff = 1, *lambda = sm->lambda, res, alpha = 0., M = 0.;
SparseMatrix Lc = NULL;
real dij, dist;
Lwdd = SparseMatrix_copy(Lwd);
m = Lw->m;
x0 = N_GNEW(dim*m,real);
if (!x0) goto RETURN;
x0 = MEMCPY(x0, x, sizeof(real)*dim*m);
y = N_GNEW(dim*m,real);
if (!y) goto RETURN;
id = Lwd->ia; jd = Lwd->ja;
d = (real*) Lwd->a;
dd = (real*) Lwdd->a;
w = (real*) Lw->a;
iw = Lw->ia; jw = Lw->ja;
#ifdef DEBUG_PRINT
if (Verbose) fprintf(stderr, "initial stress = %f\n", get_stress(m, dim, iw, jw, w, d, x, sm->scaling, sm->data, 1));
#endif
/* for the additional matrix L due to the position constraints */
if (sm->scheme == SM_SCHEME_NORMAL_ELABEL){
get_edge_label_matrix(sm->data, m, dim, x, &Lc, &x00);
if (Lc) Lw = SparseMatrix_add(Lw, Lc);
} else if (sm->scheme == SM_SCHEME_UNIFORM_STRESS){
alpha = ((real*) (sm->data))[0];
M = ((real*) (sm->data))[1];
}
while (iter++ < maxit_sm && diff > tol){
#ifdef GVIEWER
if (Gviewer) {
drawScene();
if (iter%2 == 0) gviewer_dump_current_frame();
}
#endif
if (sm->scheme != SM_SCHEME_STRESS_APPROX){
for (i = 0; i < m; i++){
idiag = -1;
diag = 0.;
for (j = id[i]; j < id[i+1]; j++){
if (i == jd[j]) {
idiag = j;
continue;
}
dist = distance(x, dim, i, jd[j]);
//if (d[j] >= -0.0001*dist){
// /* sometimes d[j] = 0 and ||x_i-x_j||=0*/
// dd[j] = d[j]/MAX(0.0001, dist);
if (d[j] == 0){
dd[j] = 0;
} else {
if (dist == 0){
dij = d[j]/w[j];/* the ideal distance */
/* perturb so points do not sit at the same place */
for (k = 0; k < dim; k++) x[jd[j]*dim+k] += 0.0001*(drand()+.0001)*dij;
dist = distance(x, dim, i, jd[j]);
}
dd[j] = d[j]/dist;
#if 0
/* if two points are at the same place, we do not want a huge entry,
as this cause problem with CG./ In any case,
at thw limit d[j] == ||x[i] - x[jd[j]]||,
or close. */
if (dist < -d[j]*0.0000001){
dd[j] = -10000.;
} else {
dd[j] = d[j]/dist;
}
#endif
}
diag += dd[j];
}
assert(idiag >= 0);
dd[idiag] = -diag;
}
/* solve (Lw+lambda*I) x = Lwdd y + lambda x0 */
SparseMatrix_multiply_dense(Lwdd, FALSE, x, FALSE, &y, FALSE, dim);
} else {
for (i = 0; i < m; i++){
for (j = 0; j < dim; j++){
y[i*dim+j] = 0;/* for stress_approx scheme, the whole rhs is calculated in stress_maxent_augment_rhs */
}
}
}
if (lambda){/* is there a penalty term? */
for (i = 0; i < m; i++){
for (j = 0; j < dim; j++){
y[i*dim+j] += lambda[i]*x0[i*dim+j];
}
}
}
/* additional term added to the rhs */
switch (sm->scheme){
case SM_SCHEME_NORMAL_ELABEL: {
for (i = 0; i < m; i++){
for (j = 0; j < dim; j++){
y[i*dim+j] += x00[i*dim+j];
}
}
break;
}
case SM_SCHEME_UNIFORM_STRESS:{/* this part can be done more efficiently using octree approximation */
uniform_stress_augment_rhs(m, dim, x, y, alpha, M);
break;
}
#if UNIMPEMENTED
case SM_SCHEME_MAXENT:{
#ifdef GVIEWER
if (Gviewer){
char *lab;
lab = MALLOC(sizeof(char)*100);
sprintf(lab,"maxent. alpha=%10.2g, iter=%d",stress_maxent_data_get_alpha(sm->data), iter);
gviewer_set_label(lab);
FREE(lab);
}
#endif
stress_maxent_augment_rhs_fast(sm, dim, x, y, &flag);
if (flag) goto RETURN;
break;
}
case SM_SCHEME_STRESS_APPROX:{
stress_approx_augment_rhs(sm, dim, x, y, &flag);
if (flag) goto RETURN;
break;
}
case SM_SCHEME_STRESS:{
#ifdef GVIEWER
if (Gviewer){
char *lab;
lab = MALLOC(sizeof(char)*100);
sprintf(lab,"pmds(k), iter=%d", iter);
gviewer_set_label(lab);
FREE(lab);
}
#endif
}
#endif /* UNIMPEMENTED */
default:
break;
}
#ifdef DEBUG_PRINT
if (Verbose) {
fprintf(stderr, "stress1 = %g\n",get_stress(m, dim, iw, jw, w, d, x, sm->scaling, sm->data, 1));
}
#endif
if (sm->scheme == SM_SCHEME_UNIFORM_STRESS){
res = uniform_stress_solve(Lw, alpha, dim, x, y, sm->tol_cg, sm->maxit_cg, &flag);
} else {
res = SparseMatrix_solve(Lw, dim, x, y, sm->tol_cg, sm->maxit_cg, SOLVE_METHOD_CG, &flag);
//res = SparseMatrix_solve(Lw, dim, x, y, sm->tol_cg, 1, SOLVE_METHOD_JACOBI, &flag);
}
if (flag) goto RETURN;
#ifdef DEBUG_PRINT
if (Verbose) fprintf(stderr, "stress2 = %g\n",get_stress(m, dim, iw, jw, w, d, y, sm->scaling, sm->data, 1));
#endif
diff = total_distance(m, dim, x, y)/sqrt(vector_product(m*dim, x, x));
#ifdef DEBUG_PRINT
if (Verbose){
fprintf(stderr, "Outer iter = %d, cg res = %g, ||x_{k+1}-x_k||/||x_k|| = %g\n",iter, res, diff);
}
#endif
MEMCPY(x, y, sizeof(real)*m*dim);
}
#ifdef DEBUG
_statistics[1] += iter-1;
#endif
#ifdef DEBUG_PRINT
if (Verbose) fprintf(stderr, "iter = %d, final stress = %f\n", iter, get_stress(m, dim, iw, jw, w, d, x, sm->scaling, sm->data, 1));
#endif
RETURN:
SparseMatrix_delete(Lwdd);
if (Lc) {
SparseMatrix_delete(Lc);
SparseMatrix_delete(Lw);
}
if (x0) FREE(x0);
if (y) FREE(y);
if (x00) FREE(x00);
return diff;
}
void country_graph_coloring(int seed, SparseMatrix A, int **p, real *norm_1){
int n = A->m, i, j, jj;
SparseMatrix L, A2;
int *ia = A->ia, *ja = A->ja;
int a = -1.;
real nrow;
real *v = NULL;
real norm1[2], norm2[2], norm11[2], norm22[2], norm = n;
real pi, pj;
int improved = TRUE;
assert(A->m == A->n);
A2 = SparseMatrix_symmetrize(A, TRUE);
ia = A2->ia; ja = A2->ja;
/* Laplacian */
L = SparseMatrix_new(n, n, 1, MATRIX_TYPE_REAL, FORMAT_COORD);
for (i = 0; i < n; i++){
nrow = 0.;
for (j = ia[i]; j < ia[i+1]; j++){
jj = ja[j];
if (jj != i){
nrow ++;
L = SparseMatrix_coordinate_form_add_entries(L, 1, &i, &jj, &a);
}
}
L = SparseMatrix_coordinate_form_add_entries(L, 1, &i, &i, &nrow);
}
L = SparseMatrix_from_coordinate_format(L);
/* largest eigen vector */
{
int maxit = 100;
real tol = 0.00001;
power_method(L, seed, maxit, tol, &v);
}
vector_ordering(n, v, p, TRUE);
/* swapping */
while (improved){
improved = FALSE; norm = n;
for (i = 0; i < n; i++){
get_local_12_norm(n, i, ia, ja, *p, norm1);
for (j = 0; j < n; j++){
if (j == i) continue;
get_local_12_norm(n, j, ia, ja, *p, norm2);
norm = MIN(norm, norm2[0]);
pi = (*p)[i]; pj = (*p)[j];
(*p)[i] = pj;
(*p)[j] = pi;
get_local_12_norm(n, i, ia, ja, *p, norm11);
get_local_12_norm(n, j, ia, ja, *p, norm22);
if (MIN(norm11[0],norm22[0]) > MIN(norm1[0],norm2[0])){
// ||
//(MIN(norm11[0],norm22[0]) == MIN(norm1[0],norm2[0]) && norm11[1]+norm22[1] > norm1[1]+norm2[1])) {
improved = TRUE;
norm1[0] = norm11[0];
norm1[1] = norm11[1];
continue;
}
(*p)[i] = pi;
(*p)[j] = pj;
}
}
if (Verbose) {
get_12_norm(n, ia, ja, *p, norm1);
fprintf(stderr, "norm = %f", norm);
fprintf(stderr, "norm1 = %f, norm2 = %f\n", norm1[0], norm1[1]);
}
}
get_12_norm(n, ia, ja, *p, norm1);
*norm_1 = norm1[0];
if (A2 != A) SparseMatrix_delete(A2);
SparseMatrix_delete(L);
}
StressMajorizationSmoother StressMajorizationSmoother2_new(SparseMatrix A, int dim, real lambda0, real *x,
int ideal_dist_scheme){
/* use up to dist 2 neighbor */
/* use up to dist 2 neighbor. This is used in overcoming pherical effect with ideal distance of
2-neighbors equal graph distance etc.
*/
StressMajorizationSmoother sm;
int i, j, k, l, m = A->m, *ia = A->ia, *ja = A->ja, *iw, *jw, *id, *jd;
int *mask, nz;
real *d, *w, *lambda;
real *avg_dist, diag_d, diag_w, dist, s = 0, stop = 0, sbot = 0;
SparseMatrix ID;
assert(SparseMatrix_is_symmetric(A, FALSE));
ID = ideal_distance_matrix(A, dim, x);
sm = GNEW(struct StressMajorizationSmoother_struct);
sm->scaling = 1.;
sm->data = NULL;
sm->scheme = SM_SCHEME_NORMAL;
sm->tol_cg = 0.01;
sm->maxit_cg = sqrt((double) A->m);
lambda = sm->lambda = N_GNEW(m,real);
for (i = 0; i < m; i++) sm->lambda[i] = lambda0;
mask = N_GNEW(m,int);
avg_dist = N_GNEW(m,real);
for (i = 0; i < m ;i++){
avg_dist[i] = 0;
nz = 0;
for (j = ia[i]; j < ia[i+1]; j++){
if (i == ja[j]) continue;
avg_dist[i] += distance(x, dim, i, ja[j]);
nz++;
}
assert(nz > 0);
avg_dist[i] /= nz;
}
for (i = 0; i < m; i++) mask[i] = -1;
nz = 0;
for (i = 0; i < m; i++){
mask[i] = i;
for (j = ia[i]; j < ia[i+1]; j++){
k = ja[j];
if (mask[k] != i){
mask[k] = i;
nz++;
}
}
for (j = ia[i]; j < ia[i+1]; j++){
k = ja[j];
for (l = ia[k]; l < ia[k+1]; l++){
if (mask[ja[l]] != i){
mask[ja[l]] = i;
nz++;
}
}
}
}
sm->Lw = SparseMatrix_new(m, m, nz + m, MATRIX_TYPE_REAL, FORMAT_CSR);
sm->Lwd = SparseMatrix_new(m, m, nz + m, MATRIX_TYPE_REAL, FORMAT_CSR);
if (!(sm->Lw) || !(sm->Lwd)) {
StressMajorizationSmoother_delete(sm);
return NULL;
}
iw = sm->Lw->ia; jw = sm->Lw->ja;
w = (real*) sm->Lw->a; d = (real*) sm->Lwd->a;
id = sm->Lwd->ia; jd = sm->Lwd->ja;
iw[0] = id[0] = 0;
nz = 0;
for (i = 0; i < m; i++){
mask[i] = i+m;
diag_d = diag_w = 0;
for (j = ia[i]; j < ia[i+1]; j++){
k = ja[j];
if (mask[k] != i+m){
mask[k] = i+m;
jw[nz] = k;
if (ideal_dist_scheme == IDEAL_GRAPH_DIST){
dist = 1;
} else if (ideal_dist_scheme == IDEAL_AVG_DIST){
dist = (avg_dist[i] + avg_dist[k])*0.5;
} else if (ideal_dist_scheme == IDEAL_POWER_DIST){
dist = pow(distance_cropped(x,dim,i,k),.4);
} else {
fprintf(stderr,"ideal_dist_scheme value wrong");
assert(0);
exit(1);
}
/*
w[nz] = -1./(ia[i+1]-ia[i]+ia[ja[j]+1]-ia[ja[j]]);
w[nz] = -2./(avg_dist[i]+avg_dist[k]);*/
/* w[nz] = -1.;*//* use unit weight for now, later can try 1/(deg(i)+deg(k)) */
w[nz] = -1/(dist*dist);
diag_w += w[nz];
jd[nz] = k;
/*
d[nz] = w[nz]*distance(x,dim,i,k);
d[nz] = w[nz]*dd[j];
d[nz] = w[nz]*(avg_dist[i] + avg_dist[k])*0.5;
*/
d[nz] = w[nz]*dist;
stop += d[nz]*distance(x,dim,i,k);
sbot += d[nz]*dist;
diag_d += d[nz];
nz++;
}
}
/* distance 2 neighbors */
for (j = ia[i]; j < ia[i+1]; j++){
k = ja[j];
for (l = ia[k]; l < ia[k+1]; l++){
if (mask[ja[l]] != i+m){
mask[ja[l]] = i+m;
if (ideal_dist_scheme == IDEAL_GRAPH_DIST){
dist = 2;
} else if (ideal_dist_scheme == IDEAL_AVG_DIST){
dist = (avg_dist[i] + 2*avg_dist[k] + avg_dist[ja[l]])*0.5;
} else if (ideal_dist_scheme == IDEAL_POWER_DIST){
dist = pow(distance_cropped(x,dim,i,ja[l]),.4);
} else {
fprintf(stderr,"ideal_dist_scheme value wrong");
assert(0);
exit(1);
}
jw[nz] = ja[l];
/*
w[nz] = -1/(ia[i+1]-ia[i]+ia[ja[l]+1]-ia[ja[l]]);
w[nz] = -2/(avg_dist[i] + 2*avg_dist[k] + avg_dist[ja[l]]);*/
/* w[nz] = -1.;*//* use unit weight for now, later can try 1/(deg(i)+deg(k)) */
w[nz] = -1/(dist*dist);
diag_w += w[nz];
jd[nz] = ja[l];
/*
d[nz] = w[nz]*(distance(x,dim,i,k)+distance(x,dim,k,ja[l]));
d[nz] = w[nz]*(dd[j]+dd[l]);
d[nz] = w[nz]*(avg_dist[i] + 2*avg_dist[k] + avg_dist[ja[l]])*0.5;
*/
d[nz] = w[nz]*dist;
stop += d[nz]*distance(x,dim,ja[l],k);
sbot += d[nz]*dist;
diag_d += d[nz];
nz++;
}
}
}
jw[nz] = i;
lambda[i] *= (-diag_w);/* alternatively don't do that then we have a constant penalty term scaled by lambda0 */
w[nz] = -diag_w + lambda[i];
jd[nz] = i;
d[nz] = -diag_d;
nz++;
iw[i+1] = nz;
id[i+1] = nz;
}
s = stop/sbot;
for (i = 0; i < nz; i++) d[i] *= s;
sm->scaling = s;
sm->Lw->nz = nz;
sm->Lwd->nz = nz;
FREE(mask);
FREE(avg_dist);
SparseMatrix_delete(ID);
return sm;
}
void SpringSmoother_delete(SpringSmoother sm){
if (!sm) return;
if (sm->D) SparseMatrix_delete(sm->D);
if (sm->ctrl) spring_electrical_control_delete(sm->ctrl);
}
/* ================================ spring and spring-electrical based smoother ================ */
SpringSmoother SpringSmoother_new(SparseMatrix A, int dim, spring_electrical_control ctrl, real *x){
SpringSmoother sm;
int i, j, k, l, m = A->m, *ia = A->ia, *ja = A->ja, *id, *jd;
int *mask, nz;
real *d, *dd;
real *avg_dist;
SparseMatrix ID = NULL;
assert(SparseMatrix_is_symmetric(A, FALSE));
ID = ideal_distance_matrix(A, dim, x);
dd = (real*) ID->a;
sm = N_GNEW(1,struct SpringSmoother_struct);
mask = N_GNEW(m,int);
avg_dist = N_GNEW(m,real);
for (i = 0; i < m ;i++){
avg_dist[i] = 0;
nz = 0;
for (j = ia[i]; j < ia[i+1]; j++){
if (i == ja[j]) continue;
avg_dist[i] += distance(x, dim, i, ja[j]);
nz++;
}
assert(nz > 0);
avg_dist[i] /= nz;
}
for (i = 0; i < m; i++) mask[i] = -1;
nz = 0;
for (i = 0; i < m; i++){
mask[i] = i;
for (j = ia[i]; j < ia[i+1]; j++){
k = ja[j];
if (mask[k] != i){
mask[k] = i;
nz++;
}
}
for (j = ia[i]; j < ia[i+1]; j++){
k = ja[j];
for (l = ia[k]; l < ia[k+1]; l++){
if (mask[ja[l]] != i){
mask[ja[l]] = i;
nz++;
}
}
}
}
sm->D = SparseMatrix_new(m, m, nz, MATRIX_TYPE_REAL, FORMAT_CSR);
if (!(sm->D)){
SpringSmoother_delete(sm);
return NULL;
}
id = sm->D->ia; jd = sm->D->ja;
d = (real*) sm->D->a;
id[0] = 0;
nz = 0;
for (i = 0; i < m; i++){
mask[i] = i+m;
for (j = ia[i]; j < ia[i+1]; j++){
k = ja[j];
if (mask[k] != i+m){
mask[k] = i+m;
jd[nz] = k;
d[nz] = (avg_dist[i] + avg_dist[k])*0.5;
d[nz] = dd[j];
nz++;
}
}
for (j = ia[i]; j < ia[i+1]; j++){
k = ja[j];
for (l = ia[k]; l < ia[k+1]; l++){
if (mask[ja[l]] != i+m){
mask[ja[l]] = i+m;
jd[nz] = ja[l];
d[nz] = (avg_dist[i] + 2*avg_dist[k] + avg_dist[ja[l]])*0.5;
d[nz] = dd[j]+dd[l];
nz++;
}
}
}
id[i+1] = nz;
}
sm->D->nz = nz;
sm->ctrl = spring_electrical_control_new();
*(sm->ctrl) = *ctrl;
sm->ctrl->random_start = FALSE;
sm->ctrl->multilevels = 1;
sm->ctrl->step /= 2;
sm->ctrl->maxiter = 20;
FREE(mask);
FREE(avg_dist);
SparseMatrix_delete(ID);
return sm;
}
real StressMajorizationSmoother_smooth(StressMajorizationSmoother sm, int dim, real *x, int maxit_sm, real tol) {
SparseMatrix Lw = sm->Lw, Lwd = sm->Lwd, Lwdd = NULL;
int i, j, m, *id, *jd, *iw, *jw, idiag, flag = 0, iter = 0;
real *w, *dd, *d, *y = NULL, *x0 = NULL, *x00 = NULL, diag, diff = 1, *lambda = sm->lambda, maxit, res, alpha = 0., M = 0.;
SparseMatrix Lc = NULL;
Lwdd = SparseMatrix_copy(Lwd);
m = Lw->m;
x0 = N_GNEW(dim*m,real);
if (!x0) goto RETURN;
x0 = MEMCPY(x0, x, sizeof(real)*dim*m);
y = N_GNEW(dim*m,real);
if (!y) goto RETURN;
id = Lwd->ia; jd = Lwd->ja;
d = (real*) Lwd->a;
dd = (real*) Lwdd->a;
w = (real*) Lw->a;
iw = Lw->ia; jw = Lw->ja;
/* for the additional matrix L due to the position constraints */
if (sm->scheme == SM_SCHEME_NORMAL_ELABEL){
get_edge_label_matrix(sm->data, m, dim, x, &Lc, &x00);
if (Lc) Lw = SparseMatrix_add(Lw, Lc);
} else if (sm->scheme == SM_SCHEME_UNIFORM_STRESS){
alpha = ((real*) (sm->data))[0];
M = ((real*) (sm->data))[1];
}
while (iter++ < maxit_sm && diff > tol){
for (i = 0; i < m; i++){
idiag = -1;
diag = 0.;
for (j = id[i]; j < id[i+1]; j++){
if (i == jd[j]) {
idiag = j;
continue;
}
dd[j] = d[j]/distance_cropped(x, dim, i, jd[j]);
diag += dd[j];
}
assert(idiag >= 0);
dd[idiag] = -diag;
}
/* solve (Lw+lambda*I) x = Lwdd y + lambda x0 */
SparseMatrix_multiply_dense(Lwdd, FALSE, x, FALSE, &y, FALSE, dim);
if (lambda){/* is there a penalty term? */
for (i = 0; i < m; i++){
for (j = 0; j < dim; j++){
y[i*dim+j] += lambda[i]*x0[i*dim+j];
}
}
}
if (sm->scheme == SM_SCHEME_NORMAL_ELABEL){
for (i = 0; i < m; i++){
for (j = 0; j < dim; j++){
y[i*dim+j] += x00[i*dim+j];
}
}
} else if (sm->scheme == SM_SCHEME_UNIFORM_STRESS){/* this part can be done more efficiently using octree approximation */
uniform_stress_augment_rhs(m, dim, x, y, alpha, M);
}
#ifdef DEBUG_PRINT
if (Verbose) fprintf(stderr, "stress1 = %f\n",get_stress(m, dim, iw, jw, w, d, x, sm->scaling, sm->data, 0));
#endif
maxit = sqrt((double) m);
if (sm->scheme == SM_SCHEME_UNIFORM_STRESS){
res = uniform_stress_solve(Lw, alpha, dim, x, y, 0.01, maxit, &flag);
} else {
res = SparseMatrix_solve(Lw, dim, x, y, 0.01, maxit, SOLVE_METHOD_CG, &flag);
}
if (flag) goto RETURN;
#ifdef DEBUG_PRINT
if (Verbose) fprintf(stderr, "stress2 = %f\n",get_stress(m, dim, iw, jw, w, d, y, sm->scaling, sm->data, 0));
#endif
diff = total_distance(m, dim, x, y)/sqrt(vector_product(m*dim, x, x));
#ifdef DEBUG_PRINT
if (Verbose){
fprintf(stderr, "Outer iter = %d, res = %g Stress Majorization diff = %g\n",iter, res, diff);
}
#endif
MEMCPY(x, y, sizeof(real)*m*dim);
}
#ifdef DEBUG
_statistics[1] += iter-1;
#endif
RETURN:
SparseMatrix_delete(Lwdd);
if (Lc) {
SparseMatrix_delete(Lc);
SparseMatrix_delete(Lw);
}
if (x0) FREE(x0);
if (y) FREE(y);
if (x00) FREE(x00);
return diff;
}
SparseMatrix DistanceMatrix_restrict_cluster(int ncluster, int *clusterp, int *cluster, SparseMatrix P, SparseMatrix R, SparseMatrix D){
#if 0
/* this construct a distance matrix of a coarse graph, for a coarsen give by merging all nodes in eahc cluster */
SparseMatrix cD = NULL;
int i, j, nzc;
int **irn, **jcn;
real **val;
int n = D->m;
int *assignment = NULL;
int nz;
int *id = D->ia, jd = D->ja;
int *mask = NULL;
int *nnodes, *mask;
real *d = NULL;
assert(D->m == D->n);
if (!D) return NULL;
if (D->a && D->type == MATRIX_TYPE_REAL) d = (real*) D->val;
irn = N_GNEW(ncluster,int*);
jcn = N_GNEW(ncluster,int*);
val = N_GNEW(ncluster,real*);
assignment = N_GNEW(n,int);
nz = N_GNEW(ncluster,int);
mask = N_GNEW(n,int);
nnodes = N_GNEW(ncluster,int);
/* find ncluster-subgrahs induced by the ncluster -clusters, find the diameter of each,
then use the radius as the distance from the supernode to the rest of the "world"
*/
for (i = 0; i < ncluster; i++) nz[i] = 0;
for (i = 0; i < ncluster; i++){
for (j = clusterp[i]; j < clusterp[i+1]; j++){
assert(clusterp[i+1] > clusterp[i]);
assignment[cluster[j]] = i;
}
}
for (i = 0; i < n; i++){/* figure out how many entries per submatrix */
ic = asignment[i];
for (j = id[i]; j < id[i+1]; j++){
if (i != jd[j] && ic == assignment[jd[j]]) {
nz[ic]++;
}
}
}
for (i = 0; i < ncluster; i++) {
irn[i] = N_GNEW(nz[i],int);
jcn[i] = N_GNEW(nz[i],int);
val[i] = N_GNEW(nz[i],int);
val[i] = NULL;
}
for (i = 0; i < ncluster; i++) nz[i] = 0;/* get subgraphs */
for (i = 0; i < n; i++) mask[i] = -1;
for (i = 0; i < ncluster; i++) nnodes[i] = -1;
for (i = 0; i < n; i++){
ic = asignment[i];
ii = mask[i];
if (ii < 0){
mask[i] = ii = nnodes[ic];
nnodes[ic]++;
}
for (j = id[i]; j < id[i+1]; j++){
jc = assignment[jd[j]];
if (i != jd[j] && ic == jc) {
jj = mask[jd[j]];
if (jj < 0){
mask[jd[j]] = jj = nnodes[jc];
nnodes[jc]++;
}
irn[ic][nz[ic]] = ii;
jcn[ic][nz[ic]] = jj;
if (d) val[ic][nz[ic]] = d[j];
}
}
}
for (i = 0; i < ncluster; i++){/* form subgraphs */
SparseMatrix A;
A = SparseMatrix_from_coordinate_arrays(nz[nz[i]], nnodes[i], nnodes[i], irn[i], jcn[i], (void*) val[i], MATRIX_TYPE_REAL);
SparseMatrix_delete(A);
}
for (i = 0; i < ncluster; i++){
for (j = clusterp[i]; j < clusterp[i+1]; j++){
assert(clusterp[i+1] > clusterp[i]);
irn[nzc] = cluster[j];
jcn[nzc] = i;
val[nzc++] = 1.;
}
}
assert(nzc == n);
cD = SparseMatrix_multiply3(R, D, P);
SparseMatrix_set_symmetric(cD);
SparseMatrix_set_pattern_symmetric(cD);
cD = SparseMatrix_remove_diagonal(cD);
FREE(nz);
FREE(assignment);
for (i = 0; i < ncluster; i++){
FREE(irn[i]);
FREE(jcn[i]);
FREE(val[i]);
}
FREE(irn); FREE(jcn); FREE(val);
FREE(mask);
FREE(nnodes);
return cD;
#endif
return NULL;
}
int main(int argc, char *argv[])
{
char *infile;
SparseMatrix B = NULL;
int dim = 2, n = 0, i, *ia, *ja, j, k, nz = 0;
real *x, *y, mean, dev, ratio, *z, r, theta, p, q, xx;
FILE *fp;
if (argc < 2){
fprintf(stderr,"Usage: similarity graph layout1 layout2\n");
}
infile = argv[1];
fp = fopen(infile,"r");
while (fscanf(fp,"%lf %lf\n",&xx, &xx) == 2) n++;
fclose(fp);
infile = argv[1];
fp = fopen(infile,"r");
x = N_GNEW(n*2,real);
for (i = 0; i < n; i++){
fscanf(fp,"%lf %lf\n",&(x[2*i]), &(x[2*i+1]));
}
fclose(fp);
infile = argv[2];
fp = fopen(infile,"r");
y = N_GNEW(n*2,real);
nz = 0;
for (i = 0; i < n; i++){
if (fscanf(fp,"%lf %lf\n",&(y[2*i]), &(y[2*i+1])) != 2) goto ERROR;
}
fclose(fp);
B = call_tri(n, 2, x);
z = N_GNEW(B->nz,real);
ia = B->ia; ja = B->ja;
nz = 0;
for (i = 0; i < n; i++){
for (j = ia[i]; j < ia[i+1]; j++){
k = ja[j];
if (k == i) continue;
z[nz++] = distance(y,dim,i,k)/distance_cropped(x,dim,i,k);
}
}
/* mean */
mean = 0;
for (i = 0; i < nz; i++) mean += z[i];
mean /= nz;
/* deviation*/
dev = 0;
for (i = 0; i < nz; i++) {
dev += (z[i]-mean)*(z[i]-mean);
}
dev /= nz;
dev = sqrt(dev);
/* bounding box area */
ratio = boundingbox_area(n, y);
/*/MAX(boundingbox_area(n, x), 0.001);*/
fprintf(stderr, "mean = %f std = %f disimilarity = %f area = %f badness = %f displacement = %f\n",
mean, dev, dev/mean, ratio, (dev/mean+1)*ratio, dispacement(n, x, y, &r, &theta, &p, &q));
fprintf(stderr, "theta = %f scaling = %f, shift = {%f, %f}\n",theta, 1/r, p, q);
printf("%.2f %.2f %.2f\n",dev/mean, dispacement(n, x, y, &r, &theta, &p, &q),ratio/1000000.);
SparseMatrix_delete(B);
FREE(x);
FREE(y);
FREE(z);
return 0;
ERROR:
printf("- - -\n");
return 0;
}
static pedge* modularity_ink_bundling(int dim, int ne, SparseMatrix B, pedge* edges, real angle_param, real angle){
int *assignment = NULL, flag, nclusters;
real modularity;
int *clusterp, *clusters;
SparseMatrix D, C;
point_t meet1, meet2;
real ink0, ink1;
pedge e;
int i, j, jj;
int use_value_for_clustering = TRUE;
//int use_value_for_clustering = FALSE;
SparseMatrix BB;
/* B may contain negative entries */
BB = SparseMatrix_copy(B);
BB = SparseMatrix_apply_fun(BB, absfun);
modularity_clustering(BB, TRUE, 0, use_value_for_clustering, &nclusters, &assignment, &modularity, &flag);
SparseMatrix_delete(BB);
#ifdef OPENGL
clusters_global = assignment;
#endif
assert(!flag);
if (Verbose > 1) fprintf(stderr, "there are %d clusters, modularity = %f\n",nclusters, modularity);
C = SparseMatrix_new(1, 1, 1, MATRIX_TYPE_PATTERN, FORMAT_COORD);
for (i = 0; i < ne; i++){
jj = assignment[i];
SparseMatrix_coordinate_form_add_entries(C, 1, &jj, &i, NULL);
}
D = SparseMatrix_from_coordinate_format(C);
SparseMatrix_delete(C);
clusterp = D->ia;
clusters = D->ja;
for (i = 0; i < nclusters; i++){
ink1 = ink(edges, clusterp[i+1] - clusterp[i], &(clusters[clusterp[i]]), &ink0, &meet1, &meet2, angle_param, angle);
if (Verbose > 1)
fprintf(stderr,"nedges = %d ink0 = %f, ink1 = %f\n",clusterp[i+1] - clusterp[i], ink0, ink1);
if (ink1 < ink0){
for (j = clusterp[i]; j < clusterp[i+1]; j++){
/* make this edge 5 points, insert two meeting points at 1 and 2, make 3 the last point */
edges[clusters[j]] = pedge_double(edges[clusters[j]]);
e = edges[clusters[j]] = pedge_double(edges[clusters[j]]);
e->x[1*dim] = meet1.x;
e->x[1*dim+1] = meet1.y;
e->x[2*dim] = meet2.x;
e->x[2*dim+1] = meet2.y;
e->x[3*dim] = e->x[4*dim];
e->x[3*dim+1] = e->x[4*dim+1];
e->npoints = 4;
}
#ifdef OPENGL
edges_global = edges;
drawScene();
#endif
}
}
SparseMatrix_delete(D);
return edges;
}
Multilevel_MQ_Clustering Multilevel_MQ_Clustering_establish(Multilevel_MQ_Clustering grid, int maxcluster) {
int *matching = grid->matching;
SparseMatrix A = grid->A;
int n = grid->n, level = grid->level, nc = 0, nclusters = n;
real mq = 0, mq_in = 0, mq_out = 0, mq_new, mq_in_new, mq_out_new, mq_max = 0, mq_in_max = 0, mq_out_max = 0;
int *ia = A->ia, *ja = A->ja;
real *a, amax = 0;
real *deg_intra = grid->deg_intra, *wgt = grid->wgt;
real *deg_intra_new, *wgt_new = NULL;
int i, j, k, jj, jc, jmax;
real *deg_inter, gain = 0, *dout = grid->dout, *dout_new, deg_in_i, deg_in_j, wgt_i, wgt_j, a_ij, dout_i, dout_j, dout_max = 0, wgt_jmax = 0;
int *mask;
real maxgain = 0;
real total_gain = 0;
SingleLinkedList *neighbors = NULL, lst;
neighbors = MALLOC(sizeof(SingleLinkedList)*n);
for (i = 0; i < n; i++) neighbors[i] = NULL;
mq = grid->mq;
mq_in = grid->mq_in;
mq_out = grid->mq_out;
deg_intra_new = MALLOC(sizeof(real)*n);
wgt_new = MALLOC(sizeof(real)*n);
deg_inter = MALLOC(sizeof(real)*n);
mask = MALLOC(sizeof(int)*n);
dout_new = MALLOC(sizeof(real)*n);
for (i = 0; i < n; i++) mask[i] = -1;
assert(n == A->n);
for (i = 0; i < n; i++) matching[i] = UNMATCHED;
/* gain in merging node A into cluster B is
mq_in_new = mq_in - |E(A,A)|/(V(A))^2 - |E(B,B)|/(V(B))^2 + (|E(A,A)|+|E(B,B)|+|E(A,B)|)/(|V(A)|+|V(B)|)^2
. = mq_in - deg_intra(A)/|A|^2 - deg_intra(B)/|B|^2 + (deg_intra(A)+deg_intra(B)+a(A,B))/(|A|+|B|)^2
mq_out_new = mq_out - |E(A,B)|/(|V(A)|*V(B)|)-\sum_{C and A connected, C!=B} |E(A,C)|/(|V(A)|*|V(C)|)-\sum_{C and B connected,C!=B} |E(B,C)|/(|V(B)|*|V(C)|)
. + \sum_{C connected to A or B, C!=A, C!=B} (|E(A,C)|+|E(B,C)|)/(|V(C)|*(|V(A)|+|V(B)|)
. = mq_out + a(A,B)/(|A|*|B|)-\sum_{C and A connected} a(A,C)/(|A|*|C|)-\sum_{C and B connected} a(B,C)/(|B|*|C|)
. + \sum_{C connected to A or B, C!=A, C!=B} (a(A,C)+a(B,C))/(|C|*(|A|+|B|))
Denote:
dout(i) = \sum_{j -- i} a(i,j)/|j|
then
mq_out_new = mq_out - |E(A,B)|/(|V(A)|*V(B)|)-\sum_{C and A connected, C!=B} |E(A,C)|/(|V(A)|*|V(C)|)-\sum_{C and B connected,C!=B} |E(B,C)|/(|V(B)|*|V(C)|)
. + \sum_{C connected to A or B, C!=A, C!=B} (|E(A,C)|+|E(B,C)|)/(|V(C)|*(|V(A)|+|V(B)|)
. = mq_out + a(A,B)/(|A|*|B|)-dout(A)/|A| - dout(B)/|B|
. + (dout(A)+dout(B))/(|A|+|B|) - (a(A,B)/|A|+a(A,B)/|B|)/(|A|+|B|)
. = mq_out -dout(A)/|A| - dout(B)/|B| + (dout(A)+dout(B))/(|A|+|B|)
after merging A and B into cluster AB,
dout(AB) = dout(A) + dout(B);
dout(C) := dout(C) - a(A,C)/|A| - a(B,C)/|B| + a(A,C)/(|A|+|B|) + a(B, C)/(|A|+|B|)
mq_new = mq_in_new/(k-1) - mq_out_new/((k-1)*(k-2))
gain = mq_new - mq
*/
a = (real*) A->a;
for (i = 0; i < n; i++) {
if (matching[i] != UNMATCHED) continue;
/* accumulate connections between i and clusters */
for (j = ia[i]; j < ia[i+1]; j++) {
jj = ja[j];
if (jj == i) continue;
if ((jc=matching[jj]) != UNMATCHED) {
if (mask[jc] != i) {
mask[jc] = i;
deg_inter[jc] = a[j];
} else {
deg_inter[jc] += a[j];
}
}
}
deg_in_i = deg_intra[i];
wgt_i = wgt[i];
dout_i = dout[i];
maxgain = 0;
jmax = -1;
for (j = ia[i]; j < ia[i+1]; j++) {
jj = ja[j];
if (jj == i) continue;
jc = matching[jj];
if (jc == UNMATCHED) {
a_ij = a[j];
wgt_j = wgt[jj];
deg_in_j = deg_intra[jj];
dout_j = dout[jj];
} else if (deg_inter[jc] < 0) {
continue;
} else {
a_ij = deg_inter[jc];
wgt_j = wgt_new[jc];
deg_inter[jc] = -1; /* so that we do not redo the calulation when we hit another neighbor in cluster jc */
deg_in_j = deg_intra_new[jc];
dout_j = dout_new[jc];
}
mq_in_new = mq_in - deg_in_i/pow(wgt_i, 2) - deg_in_j/pow(wgt_j,2)
+ (deg_in_i + deg_in_j + a_ij)/pow(wgt_i + wgt_j,2);
mq_out_new = mq_out - dout_i/wgt_i - dout_j/wgt_j + (dout_i + dout_j)/(wgt_i + wgt_j);
if (nclusters > 2) {
mq_new = 2*(mq_in_new/(nclusters - 1) - mq_out_new/((nclusters - 1)*(nclusters - 2)));
} else {
mq_new = 2*mq_in_new/(nclusters - 1);
}
#ifdef DEBUG
{ int ncluster;
double mq2, mq_in2, mq_out2, *dout2;
int *matching2, nc2 = nc;
matching2 = MALLOC(sizeof(int)*A->m);
matching2 = MEMCPY(matching2, matching, sizeof(real)*A->m);
if (jc != UNMATCHED) {
matching2[i] = jc;
} else {
matching2[i] = nc2;
matching2[jj] = nc2;
nc2++;
}
for (k = 0; k < n; k++) if (matching2[k] == UNMATCHED) matching2[k] =nc2++;
mq2 = get_mq(A, matching2, &ncluster, &mq_in2, &mq_out2, &dout2);
fprintf(stderr," {dout_i, dout_j}={%f,%f}, {predicted, calculated}: mq = {%f, %f}, mq_in ={%f,%f}, mq_out = {%f,%f}\n",dout_i, dout_j, mq_new, mq2, mq_in_new, mq_in2, mq_out_new, mq_out2);
mq_new = mq2;
}
#endif
gain = mq_new - mq;
if (Verbose) fprintf(stderr,"gain in merging node %d with node %d = %f-%f = %f\n", i, jj, mq, mq_new, gain);
if (j == ia[i] || gain > maxgain) {
maxgain = gain;
jmax = jj;
amax = a_ij;
dout_max = dout_j;
wgt_jmax = wgt_j;
mq_max = mq_new;
mq_in_max = mq_in_new;
mq_out_max = mq_out_new;
}
}
/* now merge i and jmax */
if (maxgain > 0 || (nc >= 1 && nc > maxcluster)) {
total_gain += maxgain;
jc = matching[jmax];
if (jc == UNMATCHED) {
fprintf(stderr, "maxgain=%f, merge %d, %d\n",maxgain, i, jmax);
neighbors[nc] = SingleLinkedList_new_int(jmax);
neighbors[nc] = SingleLinkedList_prepend_int(neighbors[nc], i);
dout_new[nc] = dout_i + dout_max;
matching[i] = matching[jmax] = nc;
wgt_new[nc] = wgt[i] + wgt[jmax];
deg_intra_new[nc] = deg_intra[i] + deg_intra[jmax] + amax;
nc++;
} else {
fprintf(stderr,"maxgain=%f, merge with existing cluster %d, %d\n",maxgain, i, jc);
neighbors[jc] = SingleLinkedList_prepend_int(neighbors[jc], i);
dout_new[jc] = dout_i + dout_max;
wgt_new[jc] += wgt[i];
matching[i] = jc;
deg_intra_new[jc] += deg_intra[i] + amax;
}
mq = mq_max;
mq_in = mq_in_max;
mq_out = mq_out_max;
nclusters--;
} else {
fprintf(stderr,"gain: %f -- no gain, skip merging node %d\n", maxgain, i);
assert(maxgain <= 0);
neighbors[nc] = SingleLinkedList_new_int(i);
matching[i] = nc;
deg_intra_new[nc] = deg_intra[i];
wgt_new[nc] = wgt[i];
nc++;
}
/* update scaled outdegree of neighbors of i and its merged node/cluster jmax */
jc = matching[i];
lst = neighbors[jc];
do {
mask[*((int*) SingleLinkedList_get_data(lst))] = n+i;
lst = SingleLinkedList_get_next(lst);
} while (lst);
lst = neighbors[jc];
do {
k = *((int*) SingleLinkedList_get_data(lst));
for (j = ia[k]; j < ia[k+1]; j++) {
jj = ja[j];
if (mask[jj] == n+i) continue;/* link to within cluster */
if ((jc = matching[jj]) == UNMATCHED) {
if (k == i) {
dout[jj] += -a[j]/wgt_i + a[j]/(wgt_i + wgt_jmax);
} else {
dout[jj] += -a[j]/wgt_jmax + a[j]/(wgt_i + wgt_jmax);
}
} else {
if (k == i) {
dout_new[jc] += -a[j]/wgt_i + a[j]/(wgt_i + wgt_jmax);
} else {
dout_new[jc] += -a[j]/wgt_jmax + a[j]/(wgt_i + wgt_jmax);
}
}
}
lst = SingleLinkedList_get_next(lst);
} while (lst);
}
fprintf(stderr,"verbose=%d\n",Verbose);
if (Verbose) fprintf(stderr,"mq = %f new mq = %f level = %d, n = %d, nc = %d, gain = %g, mq_in = %f, mq_out = %f\n", mq, mq + total_gain,
level, n, nc, total_gain, mq_in, mq_out);
#ifdef DEBUG
{ int ncluster;
mq = get_mq(A, matching, &ncluster, &mq_in, &mq_out, &dout);
fprintf(stderr," mq = %f\n",mq);
}
#endif
if (nc >= 1 && (total_gain > 0 || nc < n)) {
/* now set up restriction and prolongation operator */
SparseMatrix P, R, R0, B, cA;
real one = 1.;
Multilevel_MQ_Clustering cgrid;
R0 = SparseMatrix_new(nc, n, 1, MATRIX_TYPE_REAL, FORMAT_COORD);
for (i = 0; i < n; i++) {
jj = matching[i];
SparseMatrix_coordinate_form_add_entries(R0, 1, &jj, &i, &one);
}
R = SparseMatrix_from_coordinate_format(R0);
SparseMatrix_delete(R0);
P = SparseMatrix_transpose(R);
B = SparseMatrix_multiply(R, A);
if (!B) goto RETURN;
cA = SparseMatrix_multiply(B, P);
if (!cA) goto RETURN;
SparseMatrix_delete(B);
grid->P = P;
grid->R = R;
level++;
cgrid = Multilevel_MQ_Clustering_init(cA, level);
deg_intra_new = REALLOC(deg_intra_new, nc*sizeof(real));
wgt_new = REALLOC(wgt_new, nc*sizeof(real));
cgrid->deg_intra = deg_intra_new;
cgrid->mq = grid->mq + total_gain;
cgrid->wgt = wgt_new;
dout_new = REALLOC(dout_new, nc*sizeof(real));
cgrid->dout = dout_new;
cgrid = Multilevel_MQ_Clustering_establish(cgrid, maxcluster);
grid->next = cgrid;
cgrid->prev = grid;
} else {
/* no more improvement, stop and final clustering found */
for (i = 0; i < n; i++) matching[i] = i;
FREE(deg_intra_new);
FREE(wgt_new);
FREE(dout_new);
}
RETURN:
for (i = 0; i < n; i++) SingleLinkedList_delete(neighbors[i], free);
FREE(neighbors);
FREE(deg_inter);
FREE(mask);
return grid;
}
pedge* edge_bundling(SparseMatrix A0, int dim, real *x, int maxit_outer, real K, int method, int nneighbor, int compatibility_method,
int max_recursion, real angle_param, real angle, int open_gl){
/* bundle edges.
A: edge graph
x: edge i is at {p,q},
. where p = x[2*dim*i : 2*dim*i+dim-1]
. and q = x[2*dim*i+dim : 2*dim*i+2*dim-1]
maxit_outer: max outer iteration for force directed bundling. Every outer iter subdivide each edge segment into 2.
K: norminal edge length in force directed bundling
method: which method to use.
nneighbor: number of neighbors to be used in forming nearest neighbor graph. Used only in agglomerative method
compatibility_method: which method to use to calculate compatibility. Used only in force directed.
max_recursion: used only in agglomerative method. Specify how many level of recursion to do to bundle bundled edges again
open_gl: whether to plot in X.
*/
int ne = A0->m;
pedge *edges;
SparseMatrix A = A0, B = NULL;
int i;
real tol = 0.001;
int k;
real step0 = 0.1, start = 0.0;
int maxit = 10;
int flag;
assert(A->n == ne);
edges = MALLOC(sizeof(pedge)*ne);
for (i = 0; i < ne; i++){
edges[i] = pedge_new(2, dim, &(x[dim*2*i]));
}
A = SparseMatrix_symmetrize(A0, TRUE);
if (Verbose) start = clock();
if (method == METHOD_INK){
/* go through the links and make sure edges are compatible */
B = check_compatibility(A, ne, edges, compatibility_method, tol);
edges = modularity_ink_bundling(dim, ne, B, edges, angle_param, angle);
} else if (method == METHOD_INK_AGGLOMERATE){
#ifdef HAVE_ANN
/* plan: merge a node with its neighbors if doing so improve. Form coarsening graph, repeat until no more ink saving */
edges = agglomerative_ink_bundling(dim, A, edges, nneighbor, max_recursion, angle_param, angle, open_gl, &flag);
assert(!flag);
#else
agerr (AGERR, "Graphviz built without approximate nearest neighbor library ANN; agglomerative inking not available\n");
edges = edges;
#endif
} else if (method == METHOD_FD){/* FD method */
/* go through the links and make sure edges are compatible */
B = check_compatibility(A, ne, edges, compatibility_method, tol);
for (k = 0; k < maxit_outer; k++){
for (i = 0; i < ne; i++){
edges[i] = pedge_double(edges[i]);
}
step0 = step0/2;
edges = force_directed_edge_bundling(B, edges, maxit, step0, K, open_gl);
}
} else if (method == METHOD_NONE){
edges = edges;
} else {
assert(0);
}
if (Verbose)
fprintf(stderr, "total edge bundling cpu = %f\n",((real) (clock() - start))/CLOCKS_PER_SEC);
if (B != A) SparseMatrix_delete(B);
if (A != A0) SparseMatrix_delete(A);
return edges;
}
TriangleSmoother TriangleSmoother_new(SparseMatrix A, int dim, real lambda0, real *x, int use_triangularization){
TriangleSmoother sm;
int i, j, k, m = A->m, *ia = A->ia, *ja = A->ja, *iw, *jw, jdiag, nz;
SparseMatrix B;
real *avg_dist, *lambda, *d, *w, diag_d, diag_w, dist;
real s = 0, stop = 0, sbot = 0;
assert(SparseMatrix_is_symmetric(A, FALSE));
avg_dist = N_GNEW(m,real);
for (i = 0; i < m ;i++){
avg_dist[i] = 0;
nz = 0;
for (j = ia[i]; j < ia[i+1]; j++){
if (i == ja[j]) continue;
avg_dist[i] += distance(x, dim, i, ja[j]);
nz++;
}
assert(nz > 0);
avg_dist[i] /= nz;
}
sm = N_GNEW(1,struct TriangleSmoother_struct);
sm->scaling = 1;
sm->data = NULL;
sm->scheme = SM_SCHEME_NORMAL;
sm->tol_cg = 0.01;
sm->maxit_cg = sqrt((double) A->m);
lambda = sm->lambda = N_GNEW(m,real);
for (i = 0; i < m; i++) sm->lambda[i] = lambda0;
if (m > 2){
if (use_triangularization){
B= call_tri(m, dim, x);
} else {
B= call_tri2(m, dim, x);
}
} else {
B = SparseMatrix_copy(A);
}
sm->Lw = SparseMatrix_add(A, B);
SparseMatrix_delete(B);
sm->Lwd = SparseMatrix_copy(sm->Lw);
if (!(sm->Lw) || !(sm->Lwd)) {
TriangleSmoother_delete(sm);
return NULL;
}
iw = sm->Lw->ia; jw = sm->Lw->ja;
w = (real*) sm->Lw->a; d = (real*) sm->Lwd->a;
for (i = 0; i < m; i++){
diag_d = diag_w = 0;
jdiag = -1;
for (j = iw[i]; j < iw[i+1]; j++){
k = jw[j];
if (k == i){
jdiag = j;
continue;
}
/* w[j] = -1./(ia[i+1]-ia[i]+ia[ja[j]+1]-ia[ja[j]]);
w[j] = -2./(avg_dist[i]+avg_dist[k]);
w[j] = -1.*/;/* use unit weight for now, later can try 1/(deg(i)+deg(k)) */
dist = pow(distance_cropped(x,dim,i,k),0.6);
w[j] = 1/(dist*dist);
diag_w += w[j];
/* d[j] = w[j]*distance(x,dim,i,k);
d[j] = w[j]*(avg_dist[i] + avg_dist[k])*0.5;*/
d[j] = w[j]*dist;
stop += d[j]*distance(x,dim,i,k);
sbot += d[j]*dist;
diag_d += d[j];
}
lambda[i] *= (-diag_w);/* alternatively don't do that then we have a constant penalty term scaled by lambda0 */
assert(jdiag >= 0);
w[jdiag] = -diag_w + lambda[i];
d[jdiag] = -diag_d;
}
s = stop/sbot;
for (i = 0; i < iw[m]; i++) d[i] *= s;
sm->scaling = s;
FREE(avg_dist);
return sm;
}
