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);
}
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);
}