static void
rvine_trees_to_vine(dml_vine_t *vine, igraph_t **trees)
{
size_t n = vine->dim;
size_t *order_inv;
gsl_vector_short *B;
igraph_integer_t Cea, Ceb;
size_t x = 0, x_hat = 0, x_hat_hat = 0; // Initialized to avoid GCC warnings.
dml_copula_t *copula = NULL; // Initialized to avoid GCC warnings.
igraph_integer_t e; // Edge id.
igraph_integer_t a, b, aa, ab, ba, bb; // Vertex id.
igraph_t **last_trees = NULL;
igraph_t *graph = NULL;
gsl_vector_short *Ue, *Ua, *Ub;
// Set the number of trees of the vines.
vine->trees = n - 1;
while (trees[vine->trees - 1] == NULL) vine->trees--;
// Nothing to do for vines without trees.
if (vine->trees == 0) return;
// Selecting a structure for the trees that were truncated.
// Is this really necessary? Think a better solution.
if (vine->trees != n - 1) {
igraph_i_set_attribute_table(&igraph_cattribute_table);
last_trees = g_malloc_n(n - 1 - vine->trees, sizeof(igraph_t *));
graph = g_malloc(sizeof(igraph_t));
for (size_t k = vine->trees; k < n - 1; k++) { // Tree index.
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++) {
// Checking the proximity condition.
igraph_edge(k <= vine->trees ? trees[k - 1] : last_trees[k - 1 - vine->trees], a, &aa, &ab);
igraph_edge(k <= vine->trees ? trees[k - 1] : last_trees[k - 1 - vine->trees], b, &ba, &bb);
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(k <= vine->trees ? trees[k - 1] : last_trees[k - 1 - vine->trees], "Ue", a);
Ub = EAP(k <= vine->trees ? trees[k - 1] : last_trees[k - 1 - vine->trees], "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 the minimum weight spanning tree.
last_trees[k - vine->trees] = g_malloc(sizeof(igraph_t));
igraph_minimum_spanning_tree_unweighted(graph, last_trees[k - vine->trees]);
igraph_destroy(graph);
}
}
order_inv = g_malloc0_n(n, sizeof(size_t));
B = gsl_vector_short_calloc(n);
// for loop in line 2.
for (size_t i = 0; i < n - 1; i++) {
if (trees[n - i - 2] == NULL) {
for (e = 0; e < igraph_ecount(last_trees[n - i - 2 - vine->trees]); e++) {
x = EAN(last_trees[n - i - 2 - vine->trees], "Cea", e);
x_hat = EAN(last_trees[n - i - 2 - vine->trees], "Ceb", e);
if (!gsl_vector_short_get(B, x - 1)
&& !gsl_vector_short_get(B, x_hat - 1)) {
x_hat = 0;
copula = NULL;
break;
}
}
} else {
for (e = 0; e < igraph_ecount(trees[n - i - 2]); e++) {
x = EAN(trees[n - i - 2], "Cea", e);
x_hat = EAN(trees[n - i - 2], "Ceb", e);
if (!gsl_vector_short_get(B, x - 1)
&& !gsl_vector_short_get(B, x_hat - 1)) {
copula = EAP(trees[n - i - 2], "copula", e);
break;
}
}
}
// Line 4.
gsl_vector_short_set(B, x - 1, 1);
vine->order[n - i - 1] = x - 1;
order_inv[x - 1] = n - i;
vine->matrix[i][i] = x;
vine->matrix[i + 1][i] = x_hat;
vine->copulas[i + 1][i] = copula;
// for loop in line 5.
for (size_t k = i + 2; k < n; k++) {
if (trees[n - k - 1] != NULL) {
for (e = 0; e < igraph_ecount(trees[n - k - 1]); e++) {
Cea = EAN(trees[n - k - 1], "Cea", e);
Ceb = EAN(trees[n - k - 1], "Ceb", e);
if (x == Cea) {
x_hat_hat = Ceb;
if (!gsl_vector_short_get(B, x_hat_hat - 1)) {
// The pseudocode of the algorithm does not included
// this check. Invalid matrices were generated when
// x_hat_hat is set to an index already assigned
// to a diagonal entry.
copula = EAP(trees[n - k - 1], "copula", e);
break;
}
} else if (x == Ceb) {
x_hat_hat = Cea;
if (!gsl_vector_short_get(B, x_hat_hat - 1)) {
// Ibdem to the previous comment.
copula = EAP(trees[n - k - 1], "copula", e);
break;
}
}
}
vine->matrix[k][i] = x_hat_hat;
vine->copulas[k][i] = copula;
}
}
}
for (size_t i = 0; i < n; i++) {
if (!gsl_vector_short_get(B, i)) {
vine->matrix[n - 1][n - 1] = i + 1;
vine->order[0] = i;
order_inv[i] = 1;
break;
}
}
// Reorder the variables. The simulation algorithm assumes that the
// diagonal entries of the R-vine matrix are ordered from n to 1.
for (size_t i = 0; i < n; i++) {
for (size_t j = 0; j <= i; j++) {
if (vine->matrix[i][j] > 0) {
vine->matrix[i][j] = order_inv[vine->matrix[i][j] - 1];
}
}
}
if (vine->trees != n - 1) {
for (size_t i = 0; i < n - 1 - vine->trees; i++) {
for (e = 0; e < igraph_ecount(last_trees[i]); e++) {
Ue = EAP(last_trees[i], "Ue", e);
gsl_vector_short_free(Ue);
}
DELEA(last_trees[i], "Ue");
igraph_destroy(last_trees[i]);
g_free(last_trees[i]);
}
g_free(last_trees);
g_free(graph);
}
g_free(order_inv);
gsl_vector_short_free(B);
}
//MCMCMC algorithm
double MC3 (int N,
gsl_matrix_short * Adj,
int Steps,
int nChains,
gsl_vector_short * BestSolution,
gsl_rng * r,
gsl_vector * lgammaLookup,
gsl_vector * logLookup){
// create the chains
gsl_vector_short * Chains[nChains];
//create copies for use by Gibbs and marginal functions
gsl_vector_short * ChainCopy = gsl_vector_short_calloc(N);
gsl_vector_short * ChainCopy2 = gsl_vector_short_calloc(N);
// create the fitness vector
gsl_vector * Marginals = gsl_vector_calloc(nChains);
//initialize swapping vector for RGF
gsl_vector_short * RGFswap = gsl_vector_short_calloc(N+1);
int i,j,k;
double BestMarginal;
BestMarginal = -1000000000.0;
//initialize chains
for(i=0; i<nChains; i++){
// allocate memory
Chains[i] = gsl_vector_short_calloc(N);
// initialize the population
Partition_Initialize(Chains[i], N, r);
}
//generate temperatures assuming uniform spacing
gsl_vector * Temps = gsl_vector_calloc(nChains);
//step size for incrementing temperatures
double StepSize;
StepSize = (COLDTEMP - HOTTEMP)/((double)nChains - 1);
gsl_vector_set(Temps, 0, HOTTEMP);
for(i=1; i<(nChains-1); i++){
gsl_vector_set(Temps, i, gsl_vector_get(Temps, i-1)+StepSize);
}
gsl_vector_set(Temps, nChains-1, COLDTEMP);
//for convenience, we want a copy of the Temps vector that doesn't
//get swapped around
gsl_vector * TempsCopy = gsl_vector_calloc(nChains);
gsl_vector_memcpy(TempsCopy, Temps);
//RGF all of the chains to start with
for(i=0; i<nChains; i++){
RGF(N, Chains[i], RGFswap);
}
int chInd1, chInd2;
double dtmp;
int itmp;
int swapFlag;
for(i=0; i<Steps; i++){
//print the best likelihood we've found so far every so often
if(i % 1000 == 0){
fprintf(stderr, "Step %d Best solution %1.4f\n", i, BestMarginal);
}
//if enough steps have passed, swap temperatures
if(i % SWAPSTEPS == 0){
//try to swap using "bucket brigade"
for(j=0; j<(nChains-1); j++){
//find which chains have adjacent temperatures
chInd1 = -1;
chInd2 = -1;
for(k=0; k<nChains; k++){
if(gsl_vector_get(TempsCopy, j) == gsl_vector_get(Temps, k)){
chInd1 = k;
}
if(gsl_vector_get(TempsCopy, j+1) == gsl_vector_get(Temps, k)){
chInd2 = k;
}
if(chInd1 >= 0 && chInd2 >=0){
break;
}
}
//try to swap them
swapFlag = TrySwap(N, Adj,
Chains[chInd1], Chains[chInd2],
ChainCopy, RGFswap,
gsl_vector_get(Temps, chInd1),
gsl_vector_get(Temps, chInd2),
r, lgammaLookup, logLookup);
if(swapFlag == 1){
dtmp = gsl_vector_get(Temps, chInd1);
gsl_vector_set(Temps, chInd1, gsl_vector_get(Temps, chInd2));
gsl_vector_set(Temps, chInd2, dtmp);
}
}
}
//take a step
for(j=0; j<nChains; j++){
dtmp = Gibbs(N, Chains[j], ChainCopy,
ChainCopy2, Adj,
gsl_vector_get(Temps, j), RGFswap,
r, lgammaLookup, logLookup);
gsl_vector_set(Marginals, j, dtmp);
}
//update the best solution, if appropriate
if(gsl_vector_max(Marginals) > BestMarginal){
itmp = gsl_vector_max_index(Marginals);
BestMarginal = gsl_vector_get(Marginals, itmp);
gsl_vector_short_memcpy(BestSolution, Chains[itmp]);
fprintf(stderr, "Steps %d Best solution %.4f\n", i, BestMarginal);
}
}
//free memory
gsl_vector_short_free(RGFswap);
gsl_vector_free(Temps);
gsl_vector_free(TempsCopy);
gsl_vector_free(Marginals);
gsl_vector_short_free(ChainCopy);
gsl_vector_short_free(ChainCopy2);
for(i=0; i<nChains; i++){
gsl_vector_short_free(Chains[i]);
}
return BestMarginal;
}
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);
}
