make perceptron automatically faster

1 files changed, 6 insertions, 15 deletions

diff --git a/training/dtrain/pairsampling.h b/training/dtrain/pairsampling.h
index 84be1efb..3f67e209 100644
--- a/training/dtrain/pairsampling.h
+++ b/training/dtrain/pairsampling.h
@@ -19,7 +19,7 @@ cmp_hyp_by_score_d(ScoredHyp a, ScoredHyp b)
 }
 
 inline void
-all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, float _unused=1)
+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();
@@ -27,6 +27,7 @@ all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, sc
   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]));
@@ -51,7 +52,7 @@ all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, sc
  */
 
 inline void
-partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, float hi_lo)
+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;
@@ -64,9 +65,7 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor
   unsigned count = 0;
   for (unsigned i = 0; i < sep_hi; i++) {
     for (unsigned j = sep_hi; j < sz; j++) {
-#ifdef DTRAIN_FASTER_PERCEPTRON
-      if ((*s)[i].model <= (*s)[j].model) {
-#endif
+      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]));
@@ -78,9 +77,6 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor
         b = true;
         break;
       }
-#ifdef DTRAIN_FASTER_PERCEPTRON
-      }
-#endif
     }
     if (b) break;
   }
@@ -88,9 +84,7 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor
   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++) {
-#ifdef DTRAIN_FASTER_PERCEPTRON
-      if ((*s)[i].model <= (*s)[j].model) {
-#endif
+      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]));
@@ -99,9 +93,6 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor
           training.push_back(make_pair((*s)[i], (*s)[j]));
       }
       if (++count == max) return;
-#ifdef DTRAIN_FASTER_PERCEPTRON
-      }
-#endif
     }
   }
 }
@@ -119,7 +110,7 @@ _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, float _unused=1)
+PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, bool _unused=false, float _also_unused=0)
 {
   unsigned max_count = 5000, count = 0, sz = s->size();
   bool b = false;