FrameIDs FileConstHandle::get_root_frames() const {
FrameIDs ret;
RMF_FOREACH(FrameID fr, get_frames()) {
if (get_parents(fr).empty()) ret.push_back(fr);
}
return ret;
}
void
parasite_widget_tree_select_widget(ParasiteWidgetTree *widget_tree,
GtkWidget *widget)
{
GtkTreeModel *model = gtk_tree_view_get_model(GTK_TREE_VIEW(widget_tree));
GList *parents = get_parents(widget, NULL);
GList *l;
GtkTreeIter iter, parent_iter = {0};
gboolean found = FALSE;
gboolean in_root = TRUE;
for (l = parents; l != NULL; l = l->next)
{
GtkWidget *cur_widget = GTK_WIDGET(l->data);
gboolean valid;
found = FALSE;
for (valid = gtk_tree_model_iter_children(model, &iter,
in_root ? NULL
: &parent_iter);
valid;
valid = gtk_tree_model_iter_next(model, &iter))
{
GtkWidget *iter_widget;
gtk_tree_model_get(model, &iter,
WIDGET, &iter_widget,
-1);
if (iter_widget == cur_widget)
{
parent_iter = iter;
in_root = FALSE;
found = TRUE;
break;
}
}
if (!found)
{
/* No good. Bail.. */
break;
}
}
if (found)
{
GtkTreePath *path = gtk_tree_model_get_path(model, &iter);
gtk_tree_view_expand_to_path(GTK_TREE_VIEW(widget_tree), path);
gtk_tree_selection_select_iter(
gtk_tree_view_get_selection(GTK_TREE_VIEW(widget_tree)),
&iter);
gtk_tree_view_scroll_to_cell(GTK_TREE_VIEW(widget_tree), path, NULL,
FALSE, 0, 0);
}
g_list_free(parents);
}
/* traverse all nodes
* find inconsitency markers for each child depending on its parents
* inconsistency is found whenever:
* edge pa --> ch
* gammax[pa,t_i] == 1 # if the parent is active, then state switch from active to passive should not occur at the child
* gammax[ch, t_i] == 1 & gammax[ch, t_(i+1)] == 0 not possible
* gammax[pa,t_i] == 0 # if the parent is passive, then state switch from passive to active should not occur at the child
* gammax[ch, t_i] == 0 & gammax[ch, t_(i+1)] == 1 not possible
* edge pa --| ch
* gammax[pa,t_i] == 1 # if parent is active, then state switch from passive to active should not occur
* gammax[ch, t_i] == 0 & gammax[ch, t_(i+1)] == 1 not possible
* gammax[pa,t_i] == 0
* gammax[ch, t_i] == 1 & gammax[ch, t_(i+1)] == 0 not possible
*
*/
int is_consistent(int *phi, int *GS, int *G, int N, int T, int R)
{
int inc=0,
cnt=0,
pcnt=0;
// initialise an array of parents, can be at most N elements
int *parents = malloc(N*sizeof(int));
// allocate consistency vector
int *consvec = malloc((T*R-1)*sizeof(int));
// for all nodes
for(int i=0; i!=N; ++i) {
// reset parents vector to 0
memset(parents, 0, N*sizeof(int));
// reset the consistency vector to all consistent
for(int ci=0; ci!=(T*R-1); ++ci) {
consvec[ci] = 1;
}
// check all parents
pcnt = get_parents(phi, N, parents, i);
if(pcnt>0) {
// for each parent
for(int j=0; j!=N; ++j) {
if(parents[j]!=0) {
checkC(phi, GS, i, j, N, T, R, consvec);
// reset -14 value to 1
for(int ci=0; ci!=(T*R-1); ci++) {
if(consvec[ci]==-14) {
consvec[ci] = 1;
}
}
}
} // parents end
// increment the inconsistency count
for(int ci=0; ci!=(T*R-1); ++ci) {
if(consvec[ci]==0) {
inc++;
}
}
}
}
free(consvec);
free(parents);
return inc;
}
static GList *
get_parents(GtkWidget *widget,
GList *parents)
{
GtkWidget *parent = gtk_widget_get_parent(widget);
parents = g_list_prepend(parents, widget);
if (parent != NULL)
return get_parents(parent, parents);
return parents;
}
/* PUBLIC */
void sb_write_clause_jmap(String_buf sb, Topform c,
int format,
I3list map)
{
Term t;
if (c->compressed)
t = NULL;
else
t = topform_to_term(c);
if (format == CL_FORM_BARE) {
if (t == NULL)
sb_append(sb, "clause_is_compressed");
else
sb_write_term(sb, t);
sb_append(sb, ".");
}
else {
if (c->id != 0) {
sb_append_id(sb, c->id, map);
sb_append(sb, " ");
}
if (t == NULL)
sb_append(sb, "clause_is_compressed");
else
sb_write_term(sb, t);
sb_append(sb, ". ");
if (format == CL_FORM_STD)
sb_write_just(sb, c->justification, map);
else {
/* CL_FORM_PARENTS */
Ilist parents = get_parents(c->justification, TRUE);
Ilist p;
sb_append(sb, "[");
for (p = parents; p; p = p->next) {
sb_append_id(sb, p->i, map);
if (p->next)
sb_append(sb, ",");
}
sb_append(sb, "].");
}
}
sb_append(sb, "\n");
if (t)
zap_term(t);
} /* sb_write_clause_jmap */
void train_once_KernelPerceptronModel(KernelPerceptron mdl, const CoNLLCorpus corpus, int max_rec) {
long match = 0, total = 0;
//size_t slen=0;
double s_initial = dsecnd();
int max_sv = 0;
log_info("Total number of training instances %d", (max_rec == -1) ? DArray_count(corpus->sentences) : max_rec);
for (int si = 0; si < ((max_rec == -1) ? DArray_count(corpus->sentences) : max_rec); si++) {
FeaturedSentence sent = (FeaturedSentence) DArray_get(corpus->sentences, si);
debug("Building feature matrix for sentence %d", si);
set_FeatureMatrix(NULL, corpus, si);
set_adjacency_matrix_fast(corpus, si, mdl, false);
max_sv += (sent->length + 1) * sent->length - sent->length;
int *model = parse(sent);
//printfarch(model, sent->length);
debug("Parsing sentence %d of length %d is done", si, sent->length);
int *empirical = get_parents(sent);
//printfarch(empirical, sent->length);
int nm = nmatch(model, empirical, sent->length);
debug("Model matches %d arcs out of %d arcs", nm, sent->length);
if (nm != sent->length) { // root has no valid parent.
log_info("Sentence %d (section %d) of length %d (%d arcs out of %d arcs are correct)", si, sent->section, sent->length, nm, sent->length);
int sentence_length = sent->length;
for (int to = 1; to <= sentence_length; to++) {
if (model[to] != empirical[to]) {
update_alpha(mdl, si, model[to], to, sent, -1);
update_alpha(mdl, si, empirical[to], to, sent, +1);
}
}
} else {
log_info("Sentence %d (section %d) of length %d (Perfect parse)", si, sent->section, sent->length);
}
size_t nsuccess;
if (budget_method == RANDOMIZED) {
if (mdl->M > budget_target) {
size_t nbefore = mdl->M;
size_t nasked = nbefore - budget_target;
nsuccess = delete_n_random_hypothesis(mdl, nasked);
log_info("%lu vectors deleted (%lu asked). Current hypothesis set size reduced from %lu to %lu", nsuccess, nasked, nbefore, mdl->M);
}
}
mdl->c++;
free_feature_matrix(corpus, si);
match += nm;
total += (sent->length);
if ((si + 1) % 1000 == 0 && si != 0) {
log_info("Running training accuracy %lf after %d sentence.", (match * 1.) / total, si + 1);
unsigned nsv = mdl->M;
log_info("%u (%f of total %d) support vectors", nsv, (nsv * 1.) / max_sv, max_sv);
}
free(model);
free(empirical);
}
unsigned nsv = mdl->M;
log_info("Running training accuracy %lf", (match * 1.) / total);
log_info("%u (%f of total %d) support vectors", nsv, (nsv * 1.) / max_sv, max_sv);
if (verbosity > 0) {
dump_support_vectors(mdl);
}
update_average_alpha(mdl);
return;
}
CubitStatus ModelEntity::remove_from_DAG(CubitBoolean recurse_flag)
{
// This counter will be used in the recursion to test whether
// the call is from outside or from the function itself. When
// the call comes from outside, the counter should always be
// zero.
CubitBoolean this_recurse = recurse_flag;
if (recurse_flag == CUBIT_FALSE) recurse_flag = CUBIT_TRUE;
DLIList<ModelEntity*> childModEntList;
// Check to see if there are no parents of this object.
if ( get_parents() == 0 )
{
if (this_recurse == CUBIT_FALSE)
{
// Since we are not recursing, this is a top-level entity.
// Notify the static observers that a top-level entity is being
// destructed. This must be done before children are disconnected.
CubitObservable *top_level = CAST_TO(this, CubitObservable);
CubitObserver::notify_static_observers(top_level, TOP_LEVEL_ENTITY_DESTRUCTED);
}
// Go through all the children and remove their link to
// the current object.
ModelEntity* tempModEntPtr = NULL ;
ModelEntity* childModEntPtr = NULL ;
childModEntList.clean_out();
disconnect_all_children(&childModEntList);
// The following while conditional may not work...it depends on
// what is_at_end does when you step over the end...CHECK THIS
int i;
for( i = 0 ; i < childModEntList.size() ; i++ )
{
// Get the next ModelEnti in the child list and make sure its
// pointer to its parent is removed.
tempModEntPtr = childModEntList.get_and_step();
// Try remove() on the child ModEnt. If it comes back with
// a success, then delete it.
childModEntPtr = tempModEntPtr;
if ( childModEntPtr->remove_from_DAG(recurse_flag) == CUBIT_SUCCESS )
{
// Now deactivate the child ModEnt
childModEntPtr->deactivated(CUBIT_TRUE) ;
// remove it from observables, just before we go to delete it
CubitObservable *observable = CAST_TO(childModEntPtr, CubitObservable);
if (observable)
{
if( !observable->notify_observers( MODEL_ENTITY_DESTRUCTED ) )
return CUBIT_FAILURE;
}
}
}
// If this is the top of the recursion, then clean out all the deactivated
// entities.
if (this_recurse == CUBIT_FALSE)
{
this->deactivated(CUBIT_TRUE) ;
// remove it from observables, just before we go to delete it
CubitObservable *observable = CAST_TO(childModEntPtr, CubitObservable);
if (observable)
{
if( !observable->notify_observers( MODEL_ENTITY_DESTRUCTED ) )
return CUBIT_FAILURE;
}
GeometryQueryTool::instance()->cleanout_deactivated_geometry() ;
}
return CUBIT_SUCCESS ;
}
else
{
return CUBIT_FAILURE ;
}
}
void train_once_PerceptronModel(PerceptronModel mdl, const CoNLLCorpus corpus, int max_rec) {
long match = 0, total = 0;
//size_t slen=0;
log_info("Total number of training instances %d", (max_rec == -1) ? DArray_count(corpus->sentences) : max_rec);
for (int si = 0; si < ((max_rec == -1) ? DArray_count(corpus->sentences) : max_rec); si++) {
//log_info("Parsing sentence %d/%d", si+1, DArray_count(corpus));
FeaturedSentence sent = (FeaturedSentence) DArray_get(corpus->sentences, si);
start(&parser_rate);
//debug("Building feature matrix for sentence %d of length %d", si, sent->length);
//set_FeatureMatrix(NULL, corpus, si);
//printfembedding(sent->feature_matrix, sent->length);
debug("Building adjacency matrix for sentence %d of length %d", si, sent->length);
build_adjacency_matrix(corpus, si, mdl->embedding_w, NULL);
//printfmatrix(sent->adjacency_matrix, sent->length);
//log_info("Adjacency matrix construction is done");
int *model = parse(sent);
stop(&parser_rate);
debug("Parsing sentence %d is done", si);
int *empirical = get_parents(sent);
/*
log_info("Model:");
printfarch(model, sent->length);
log_info("Empirical:");
printfarch(empirical, sent->length);
*/
int nm = nmatch(model, empirical, sent->length);
debug("Model matches %d arcs out of %d arcs", nm, sent->length);
if (nm != sent->length) { // root has no valid parent.
if (corpus->disrete_patterns_parts) {
log_info("I have discrete features");
DArray* model_features = DArray_create(sizeof (uint32_t), 16);
DArray* empirical_features = DArray_create(sizeof (uint32_t), 16);
for (int fi = 1; fi < sent->length; fi++) {
fill_features(mdl->features->map, model_features, model[fi], fi, sent);
fill_features(mdl->features->map, empirical_features, empirical[fi], fi, sent);
}
for (int i = 0; i < DArray_count(model_features); i++) {
uint32_t *fidx = (uint32_t *) DArray_get(model_features, i);
mdl->discrete_w->data[*fidx] -= 1.0;
mdl->discrete_w_avg->data[*fidx] -= (mdl->c) * 1.0;
mdl->discrete_w_temp->data[*fidx] -= 1.0;
}
for (int i = 0; i < DArray_count(empirical_features); i++) {
uint32_t *fidx = (uint32_t *) DArray_get(empirical_features, i);
mdl->discrete_w->data[*fidx] += 1.0;
mdl->discrete_w_avg->data[*fidx] += (mdl->c) * 1.0;
mdl->discrete_w_temp->data[*fidx] += 1.0;
}
DArray_destroy(model_features);
DArray_destroy(empirical_features);
}
for (int i = 1; i <= sent->length; i++) {
if (model[i] != empirical[i]){
// -1 for Model arch
embedding_feature(sent, model[i], i, xformed_v);
vadd(mdl->embedding_w, xformed_v, -1.0);
vadd(mdl->embedding_w_temp, xformed_v, -1.0);
vadd(mdl->embedding_w_avg, xformed_v, -(mdl->c));
// +1 for Gold arc
embedding_feature(sent, empirical[i], i, xformed_v);
vadd(mdl->embedding_w, xformed_v, 1.0);
vadd(mdl->embedding_w_temp, xformed_v, 1.0);
vadd(mdl->embedding_w_avg, xformed_v, (mdl->c));
}
//free(real_embedding);
//free(model_embedding);
}
}
free_feature_matrix(corpus, si);
mdl->c++;
match += nm;
total += (sent->length);
if (si % 1000 == 0 && si > 0) {
log_info("Running training accuracy %lf", (match * 1.) / total);
}
free(model);
free(empirical);
//free_sentence_structures(sent);
}
log_info("Running training accuracy %lf", (match * 1.) / total);
if (corpus->disrete_patterns_parts) {
for (int i = 0; i < mdl->n; i++) {
// mdl->w_avg[i] /= (numit * DArray_count(corpus));
//mdl->w[i] -=(mdl->w_avg[i])/(mdl->c);
mdl->discrete_w_temp->data[i] = mdl->discrete_w->data[i] - (mdl->discrete_w_avg->data[i]) / (mdl->c);
}
}
//vadd(mdl->w_cont, mdl->w_cont_avg, -1./(mdl->c), SCODE_FEATURE_VECTOR_LENGTH);
memcpy(mdl->embedding_w_temp->data, mdl->embedding_w->data, mdl->embedding_w->n * sizeof (float));
vadd(mdl->embedding_w_temp, mdl->embedding_w_avg, -1. / (mdl->c));
// free(mdl->w);
// mdl->w = mdl->w_avg;
//free_feature_matrix(sent);
}