dtrain: removed old stuff

1 files changed, 0 insertions, 141 deletions

diff --git a/training/dtrain/pairsampling.h b/training/dtrain/pairsampling.h
deleted file mode 100644
index 1a3c498c..00000000
--- a/training/dtrain/pairsampling.h
+++ /dev/null
@@ -1,141 +0,0 @@
-#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;
-}
-
-bool
-cmp_hyp_by_score_d(ScoredHyp a, ScoredHyp b)
-{
-  return a.score > b.score;
-}
-
-inline void
-all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, bool misranked_only, float _unused=1)
-{
-  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
-  unsigned sz = s->size();
-  bool b = false;
-  unsigned count = 0;
-  for (unsigned i = 0; i < sz-1; i++) {
-    for (unsigned j = i+1; j < sz; j++) {
-      if (misranked_only && !((*s)[i].model <= (*s)[j].model)) continue;
-      if (threshold > 0) {
-        if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      } else {
-        if ((*s)[i].score != (*s)[j].score)
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      }
-      if (++count == max) {
-        b = true;
-        break;
-      }
-    }
-    if (b) break;
-  }
-}
-
-/*
- * multipartite ranking
- *  sort (descending) by bleu
- *  compare top X to middle Y and low X
- *  cmp middle Y to low X
- */
-
-inline void
-partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, bool misranked_only, float hi_lo)
-{
-  unsigned sz = s->size();
-  if (sz < 2) return;
-  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
-  unsigned sep = round(sz*hi_lo);
-  unsigned sep_hi = sep;
-  if (sz > 4) while (sep_hi < sz && (*s)[sep_hi-1].score == (*s)[sep_hi].score) ++sep_hi;
-  else sep_hi = 1;
-  bool b = false;
-  unsigned count = 0;
-  for (unsigned i = 0; i < sep_hi; i++) {
-    for (unsigned j = sep_hi; j < sz; j++) {
-      if (misranked_only && !((*s)[i].model <= (*s)[j].model)) continue;
-      if (threshold > 0) {
-        if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      } else {
-        if ((*s)[i].score != (*s)[j].score)
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      }
-      if (++count == max) {
-        b = true;
-        break;
-      }
-    }
-    if (b) break;
-  }
-  unsigned sep_lo = sz-sep;
-  while (sep_lo > 0 && (*s)[sep_lo-1].score == (*s)[sep_lo].score) --sep_lo;
-  for (unsigned i = sep_hi; i < sz-sep_lo; i++) {
-    for (unsigned j = sz-sep_lo; j < sz; j++) {
-      if (misranked_only && !((*s)[i].model <= (*s)[j].model)) continue;
-      if (threshold > 0) {
-        if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      } else {
-        if ((*s)[i].score != (*s)[j].score)
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      }
-      if (++count == max) return;
-    }
-  }
-}
-
-/*
- * pair sampling as in
- * 'Tuning as Ranking' (Hopkins & May, 2011)
- *     count = 5000
- * threshold = 5% BLEU (0.05 for param 3)
- *       cut = top 50
- */
-bool
-_PRO_cmp_pair_by_diff_d(pair<ScoredHyp,ScoredHyp> a, pair<ScoredHyp,ScoredHyp> b)
-{
-  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, unsigned max, bool _unused=false, float _also_unused=0)
-{
-  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
-  unsigned max_count = 5000, count = 0, sz = s->size();
-  bool b = false;
-  for (unsigned i = 0; i < sz-1; i++) {
-    for (unsigned j = i+1; j < sz; 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_d);
-    training.erase(training.begin()+50, training.end());
-  }
-  return;
-}
-
-
-} // namespace
-
-#endif
-