diff options
Diffstat (limited to 'training/dtrain/pairsampling.h')
| -rw-r--r-- | training/dtrain/pairsampling.h | 21 |
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; |