int main() {
igraph_t g, tree;
igraph_vector_t eb, edges;
long int i;
igraph_small(&g, 0, IGRAPH_UNDIRECTED,
0, 1, 0, 2, 0, 3, 0, 4, 0, 5,
0, 6, 0, 7, 0, 8, 0, 10, 0, 11,
0, 12, 0, 13, 0, 17, 0, 19, 0, 21,
0, 31, 1, 2, 1, 3, 1, 7, 1, 13,
1, 17, 1, 19, 1, 21, 1, 30, 2, 3,
2, 7, 2, 8, 2, 9, 2, 13, 2, 27,
2, 28, 2, 32, 3, 7, 3, 12, 3, 13,
4, 6, 4, 10, 5, 6, 5, 10, 5, 16,
6, 16, 8, 30, 8, 32, 8, 33, 9, 33,
13, 33, 14, 32, 14, 33, 15, 32, 15, 33,
18, 32, 18, 33, 19, 33, 20, 32, 20, 33,
22, 32, 22, 33, 23, 25, 23, 27, 23, 29,
23, 32, 23, 33, 24, 25, 24, 27, 24, 31,
25, 31, 26, 29, 26, 33, 27, 33, 28, 31,
28, 33, 29, 32, 29, 33, 30, 32, 30, 33,
31, 32, 31, 33, 32, 33,
-1);
igraph_vector_init(&eb, igraph_ecount(&g));
igraph_edge_betweenness(&g, &eb, IGRAPH_UNDIRECTED, /*weights=*/ 0);
for (i=0; i<igraph_vector_size(&eb); i++) {
VECTOR(eb)[i] = -VECTOR(eb)[i];
}
igraph_minimum_spanning_tree_prim(&g, &tree, &eb);
igraph_write_graph_edgelist(&tree, stdout);
igraph_vector_init(&edges, 0);
igraph_minimum_spanning_tree(&g, &edges, &eb);
igraph_vector_print(&edges);
igraph_vector_destroy(&edges);
igraph_destroy(&tree);
igraph_destroy(&g);
igraph_vector_destroy(&eb);
return 0;
}
static void
fit_rvine_trees(igraph_t **trees,
const gsl_matrix *data,
const dml_vine_weight_t weight,
const dml_vine_trunc_t trunc,
const dml_copula_indeptest_t indeptest,
const double indeptest_level,
const dml_copula_type_t *types,
const size_t types_size,
const dml_copula_select_t select,
const gsl_rng *rng)
{
size_t m, n;
igraph_t *graph;
igraph_vector_t *graph_weight;
dml_copula_t *copula;
gsl_vector *x;
igraph_integer_t e; // Edge id.
igraph_integer_t a, aa, ab, b, ba, bb; // Vertex id.
gsl_vector *u = NULL, *v = NULL;
igraph_integer_t Cea, Ceb;
gsl_vector_short *Ue, *Ua, *Ub;
size_t k;
dml_measure_t *measure;
double tree_aic, copula_aic;
gsl_permutation *perm, *rank, *u_rank = NULL, *v_rank = NULL;
igraph_i_set_attribute_table(&igraph_cattribute_table);
m = data->size1;
n = data->size2;
graph = g_malloc(sizeof(igraph_t));
graph_weight = g_malloc(sizeof(igraph_vector_t));
perm = gsl_permutation_alloc(m);
for (k = 0; k < n - 1; k++) { // Tree index.
if (k == 0) {
igraph_full(graph, n, IGRAPH_UNDIRECTED, IGRAPH_NO_LOOPS);
// Assign the observations to the nodes.
for (size_t i = 0; i < n; i++) { // Variable and node index.
x = gsl_vector_alloc(m);
gsl_matrix_get_col(x, data, i);
// Results of the h-function of the copula assigned to the
// edge that corresponds to this vertex in the previous tree.
// h for the h-function with its arguments in order and
// hrev for the h-function with its arguments reversed. In the
// first tree both are equal to the observations of the
// corresponding variable, in the rest of the trees they differ.
SETVAP(graph, "h", i, x);
SETVAP(graph, "hrev", i, x);
gsl_sort_vector_index(perm, x);
rank = gsl_permutation_alloc(m);
gsl_permutation_inverse(rank, perm);
// Ranks of the h and hrev vectors.
SETVAP(graph, "hrank", i, rank);
SETVAP(graph, "hrevrank", i, rank);
}
for (e = 0; e < igraph_ecount(graph); e++) {
igraph_edge(graph, e, &a, &b);
// Variables "connected" by this edge.
Ue = gsl_vector_short_calloc(n);
gsl_vector_short_set(Ue, a, 1);
gsl_vector_short_set(Ue, b, 1);
SETEAP(graph, "Ue", e, Ue);
// Conditioned set.
SETEAN(graph, "Cea", e, a + 1);
SETEAN(graph, "Ceb", e, b + 1);
Cea = EAN(graph, "Cea", e);
Ceb = EAN(graph, "Ceb", e);
// Calculate the weight of the edge.
u = VAP(graph, "h", a);
v = VAP(graph, "h", b);
u_rank = VAP(graph, "hrank", a);
v_rank = VAP(graph, "hrank", b);
// The conditioned set is ordered to make the order of the
// arguments in the bivariate copulas unique as suggested in
// Czado, C. (2010) Pair-Copula Constructions of Multivariate
// Copulas. In Jaworski, P. and Durante, F. and Hardle, W. K.
// and Rychlik, T. (eds.) Copula Theory and Its Applications,
// Springer-Verlag, 93-109.
if (Cea < Ceb) {
rvine_set_weight(graph, weight, e, u, v, u_rank, v_rank);
} else {
rvine_set_weight(graph, weight, e, v, u, v_rank, u_rank);
}
}
} else {
igraph_empty(graph, n - k, IGRAPH_UNDIRECTED);
// Adding all "possible" edges.
for (a = 0; a < igraph_vcount(graph) - 1; a++) {
for (b = a + 1; b < igraph_vcount(graph); b++) {
igraph_edge(trees[k - 1], a, &aa, &ab);
igraph_edge(trees[k - 1], b, &ba, &bb);
// Checking the proximity condition.
if (aa == ba || aa == bb || ab == ba || ab == bb) {
igraph_add_edge(graph, a, b);
igraph_get_eid(graph, &e, a, b, IGRAPH_UNDIRECTED, 1);
// Variables "connected" by this edge and conditioned set.
Ua = EAP(trees[k - 1], "Ue", a);
Ub = EAP(trees[k - 1], "Ue", b);
Ue = gsl_vector_short_calloc(n);
for (size_t i = 0; i < n; i++) {
gsl_vector_short_set(Ue, i,
gsl_vector_short_get(Ua, i)
| gsl_vector_short_get(Ub, i));
if (gsl_vector_short_get(Ua, i)
&& !gsl_vector_short_get(Ub, i)) {
SETEAN(graph, "Cea", e, i + 1);
}
if (gsl_vector_short_get(Ub, i)
&& !gsl_vector_short_get(Ua, i)) {
SETEAN(graph, "Ceb", e, i + 1);
}
}
SETEAP(graph, "Ue", e, Ue);
}
}
}
// Compute pseudo-observations and edge weights.
for (a = 0; a < igraph_vcount(graph); a++) {
// See the comment in the code for the first tree.
SETVAP(graph, "h", a, NULL);
SETVAP(graph, "hrev", a, NULL);
SETVAP(graph, "hrank", a, NULL);
SETVAP(graph, "hrevrank", a, NULL);
}
for (e = 0; e < igraph_ecount(graph); e++) {
igraph_edge(graph, e, &a, &b);
Cea = EAN(graph, "Cea", e);
Ceb = EAN(graph, "Ceb", e);
// Assign u and u_rank.
if ((Cea == EAN(trees[k - 1], "Cea", a)
&& (EAN(trees[k - 1], "Cea", a)
< EAN(trees[k - 1], "Ceb", a)))
|| (Cea != EAN(trees[k - 1], "Cea", a)
&& (EAN(trees[k - 1], "Cea", a)
> EAN(trees[k - 1], "Ceb", a)))) {
u = VAP(graph, "h", a);
if (u == NULL) {
copula = EAP(trees[k - 1], "copula", a);
measure = EAP(trees[k - 1], "measure", a);
u = gsl_vector_alloc(m);
dml_copula_h(copula, measure->x, measure->y, u);
SETVAP(graph, "h", a, u);
gsl_sort_vector_index(perm, u);
rank = gsl_permutation_alloc(m);
gsl_permutation_inverse(rank, perm);
SETVAP(graph, "hrank", a, rank);
}
u_rank = VAP(graph, "hrank", a);
}
if ((Cea == EAN(trees[k - 1], "Cea", a)
&& (EAN(trees[k - 1], "Cea", a)
> EAN(trees[k - 1], "Ceb", a)))
|| (Cea != EAN(trees[k - 1], "Cea", a)
&& (EAN(trees[k - 1], "Cea", a)
< EAN(trees[k - 1], "Ceb", a)))) {
u = VAP(graph, "hrev", a);
if (u == NULL) {
copula = EAP(trees[k - 1], "copula", a);
measure = EAP(trees[k - 1], "measure", a);
u = gsl_vector_alloc(m);
dml_copula_h(copula, measure->y, measure->x, u);
SETVAP(graph, "hrev", a, u);
gsl_sort_vector_index(perm, u);
rank = gsl_permutation_alloc(m);
gsl_permutation_inverse(rank, perm);
SETVAP(graph, "hrevrank", a, rank);
}
u_rank = VAP(graph, "hrevrank", a);
}
// Assign v and v_rank.
if ((Ceb == EAN(trees[k - 1], "Cea", b)
&& (EAN(trees[k - 1], "Cea", b)
< EAN(trees[k - 1], "Ceb", b)))
|| (Ceb != EAN(trees[k - 1], "Cea", b)
&& (EAN(trees[k - 1], "Cea", b)
> EAN(trees[k - 1], "Ceb", b)))) {
v = VAP(graph, "h", b);
if (v == NULL) {
copula = EAP(trees[k - 1], "copula", b);
measure = EAP(trees[k - 1], "measure", b);
v = gsl_vector_alloc(m);
dml_copula_h(copula, measure->x, measure->y, v);
SETVAP(graph, "h", b, v);
gsl_sort_vector_index(perm, v);
rank = gsl_permutation_alloc(m);
gsl_permutation_inverse(rank, perm);
SETVAP(graph, "hrank", b, rank);
}
v_rank = VAP(graph, "hrank", b);
}
if ((Ceb == EAN(trees[k - 1], "Cea", b)
&& (EAN(trees[k - 1], "Cea", b)
> EAN(trees[k - 1], "Ceb", b)))
|| (Ceb != EAN(trees[k - 1], "Cea", b)
&& (EAN(trees[k - 1], "Cea", b)
< EAN(trees[k - 1], "Ceb", b)))) {
v = VAP(graph, "hrev", b);
if (v == NULL) {
copula = EAP(trees[k - 1], "copula", b);
measure = EAP(trees[k - 1], "measure", b);
v = gsl_vector_alloc(m);
dml_copula_h(copula, measure->y, measure->x, v);
SETVAP(graph, "hrev", b, v);
gsl_sort_vector_index(perm, v);
rank = gsl_permutation_alloc(m);
gsl_permutation_inverse(rank, perm);
SETVAP(graph, "hrevrank", b, rank);
}
v_rank = VAP(graph, "hrevrank", b);
}
// Set the weight of the edge. The arguments are ordered here.
// The order determines the x and y fields of measure.
if (Cea < Ceb) {
rvine_set_weight(graph, weight, e, u, v, u_rank, v_rank);
} else {
rvine_set_weight(graph, weight, e, v, u, v_rank, u_rank);
}
}
}
// Compute the minimum weight spanning tree.
trees[k] = g_malloc(sizeof(igraph_t));
igraph_vector_init(graph_weight, igraph_ecount(graph));
EANV(graph, "weight", graph_weight);
igraph_minimum_spanning_tree_prim(graph, trees[k], graph_weight);
igraph_vector_destroy(graph_weight);
tree_aic = 0;
for (e = 0; e < igraph_ecount(trees[k]); e++) {
igraph_edge(trees[k], e, &a, &b);
Cea = EAN(trees[k], "Cea", e);
Ceb = EAN(trees[k], "Ceb", e);
measure = EAP(trees[k], "measure", e);
// Assign a bivariate copula to the edge.
if (Cea < Ceb) {
copula = dml_copula_select(measure->x, measure->y, measure,
indeptest, indeptest_level, types,
types_size, select, rng);
// Get information for the truncation of the vine.
if (trunc == DML_VINE_TRUNC_AIC) {
dml_copula_aic(copula, measure->x, measure->y, &copula_aic);
tree_aic += copula_aic;
}
} else {
copula = dml_copula_select(measure->y, measure->x, measure,
indeptest, indeptest_level, types,
types_size, select, rng);
// Get information for the truncation of the vine.
if (trunc == DML_VINE_TRUNC_AIC) {
dml_copula_aic(copula, measure->y, measure->x, &copula_aic);
tree_aic += copula_aic;
}
}
SETEAP(trees[k], "copula", e, copula);
}
igraph_destroy(graph);
// Check if the vine should be truncated.
if (trunc == DML_VINE_TRUNC_AIC && tree_aic >= 0) {
// Free the memory used for the last tree.
rvine_tree_cleanup(trees[k]);
for (e = 0; e < igraph_ecount(trees[k]); e++) {
copula = EAP(trees[k], "copula", e);
dml_copula_free(copula);
}
igraph_destroy(trees[k]);
g_free(trees[k]);
trees[k] = NULL;
break;
}
if (k > 0) rvine_tree_cleanup(trees[k - 1]);
}
// Cleanup the last tree if the vine was completely estimated.
// If the vine was truncated, the last tree will be freed in
// the function vine_fit_rvine, because the rvine_trees_to_vine
// function needs some attributes of its edges.
if (k == n - 1) {
rvine_tree_cleanup(trees[n - 2]);
}
g_free(graph_weight);
g_free(graph);
gsl_permutation_free(perm);
}
