1 files changed, 119 insertions, 0 deletions
diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h
new file mode 100644
index 00000000..1fc5b8a0
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+++ b/dtrain/pairsampling.h
@@ -0,0 +1,119 @@
+#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%
+ *  cmp middle 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
+