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diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h
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+#ifndef _DTRAIN_PAIRSAMPLING_H_
+#define _DTRAIN_PAIRSAMPLING_H_
+
+namespace dtrain
+{
+
+
+bool
+accept_pair(score_t a, score_t b, score_t threshold)
+{
+ if (fabs(a - b) < threshold) return false;
+ return true;
+}
+
+inline void
+all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold)
+{
+ for (unsigned i = 0; i < s->size()-1; i++) {
+ for (unsigned j = i+1; j < s->size(); j++) {
+ if (threshold > 0) {
+ if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
+ training.push_back(make_pair((*s)[i], (*s)[j]));
+ }
+ } else {
+ training.push_back(make_pair((*s)[i], (*s)[j]));
+ }
+ }
+ }
+}
+
+/*
+ * multipartite ranking
+ * sort by bleu
+ * compare top 10% to middle 80% and low 10%
+ * 80% to low 10%
+ */
+bool
+_108010_cmp_hyp_by_score(ScoredHyp a, ScoredHyp b)
+{
+ return a.score < b.score;
+}
+inline void
+part108010(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold)
+{
+ sort(s->begin(), s->end(), _108010_cmp_hyp_by_score);
+ unsigned sz = s->size();
+ unsigned slice = 10;
+ unsigned sep = sz%slice;
+ if (sep == 0) sep = sz/slice;
+ for (unsigned i = 0; i < sep; i++) {
+ for (unsigned j = sep; j < sz; j++) {
+ if ((*s)[i].rank < (*s)[j].rank) {
+ if (threshold > 0) {
+ if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
+ training.push_back(make_pair((*s)[i], (*s)[j]));
+ }
+ } else {
+ training.push_back(make_pair((*s)[i], (*s)[j]));
+ }
+ }
+ }
+ }
+ for (unsigned i = sep; i < sz-sep; i++) {
+ for (unsigned j = sz-sep; j < sz; j++) {
+ if ((*s)[i].rank < (*s)[j].rank) {
+ if (threshold > 0) {
+ if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
+ training.push_back(make_pair((*s)[i], (*s)[j]));
+ }
+ } else {
+ training.push_back(make_pair((*s)[i], (*s)[j]));
+ }
+ }
+ }
+ }
+}
+
+/*
+ * pair sampling as in
+ * 'Tuning as Ranking' (Hopkins & May, 2011)
+ * count = 5000
+ * threshold = 5% BLEU
+ * cut = top 50
+ */
+bool
+_PRO_cmp_pair_by_diff(pair<ScoredHyp,ScoredHyp> a, pair<ScoredHyp,ScoredHyp> b)
+{
+ // descending order
+ return (fabs(a.first.score - a.second.score)) > (fabs(b.first.score - b.second.score));
+}
+inline void
+PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold=0.05)
+{
+ unsigned max_count = 5000, count = 0;
+ bool b = false;
+ for (unsigned i = 0; i < s->size()-1; i++) {
+ for (unsigned j = i+1; j < s->size(); j++) {
+ if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
+ training.push_back(make_pair((*s)[i], (*s)[j]));
+ if (++count == max_count) {
+ b = true;
+ break;
+ }
+ }
+ }
+ if (b) break;
+ }
+ if (training.size() > 50) {
+ sort(training.begin(), training.end(), _PRO_cmp_pair_by_diff);
+ training.erase(training.begin()+50, training.end());
+ }
+ return;
+}
+
+
+} // namespace
+
+#endif
+