diff options
Diffstat (limited to 'dtrain/pairsampling.h')
| -rw-r--r-- | dtrain/pairsampling.h | 35 |
1 files changed, 17 insertions, 18 deletions
diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h index 502901af..9774ba4a 100644 --- a/dtrain/pairsampling.h +++ b/dtrain/pairsampling.h @@ -1,9 +1,8 @@ -#ifndef _DTRAIN_SAMPLE_H_ -#define _DTRAIN_SAMPLE_H_ - +#ifndef _DTRAIN_PAIRSAMPLING_H_ +#define _DTRAIN_PAIRSAMPLING_H_ #include "kbestget.h" - +#include "sampler.h" // cdec MT19937 namespace dtrain { @@ -11,19 +10,18 @@ namespace dtrain struct TPair { - SparseVector<double> first, second; - size_t first_rank, second_rank; - double first_score, second_score; + SparseVector<double> first, second; + size_t first_rank, second_rank; + double first_score, second_score; }; typedef vector<TPair> TrainingInstances; - void -sample_all( KBestList* kb, TrainingInstances &training ) +sample_all_pairs(KBestList* kb, TrainingInstances &training) { - for ( size_t i = 0; i < kb->GetSize()-1; i++ ) { - for ( size_t j = i+1; j < kb->GetSize(); j++ ) { + for (size_t i = 0; i < kb->GetSize()-1; i++) { + for (size_t j = i+1; j < kb->GetSize(); j++) { TPair p; p.first = kb->feats[i]; p.second = kb->feats[j]; @@ -31,18 +29,18 @@ sample_all( KBestList* kb, TrainingInstances &training ) p.second_rank = j; p.first_score = kb->scores[i]; p.second_score = kb->scores[j]; - training.push_back( p ); + training.push_back(p); } } } void -sample_rand( KBestList* kb, TrainingInstances &training ) +sample_rand_pairs(KBestList* kb, TrainingInstances &training, MT19937* prng) { - srand( time(NULL) ); - for ( size_t i = 0; i < kb->GetSize()-1; i++ ) { - for ( size_t j = i+1; j < kb->GetSize(); j++ ) { - if ( rand() % 2 ) { + srand(time(NULL)); + for (size_t i = 0; i < kb->GetSize()-1; i++) { + for (size_t j = i+1; j < kb->GetSize(); j++) { + if (prng->next() < .5) { TPair p; p.first = kb->feats[i]; p.second = kb->feats[j]; @@ -50,10 +48,11 @@ sample_rand( KBestList* kb, TrainingInstances &training ) p.second_rank = j; p.first_score = kb->scores[i]; p.second_score = kb->scores[j]; - training.push_back( p ); + training.push_back(p); } } } + cout << training.size() << " sampled" << endl; } |