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#ifndef _DTRAIN_SAMPLE_H_
#define _DTRAIN_SAMPLE_H_
#include "kbestget.h"
namespace dtrain
{
struct TPair
{
SparseVector<double> first, second;
size_t first_rank, second_rank;
double first_score, second_score;
double model_score_diff;
double loss_diff;
};
typedef vector<TPair> TrainingInstances;
void
sample_all( KBestList* kb, TrainingInstances &training, size_t n_pairs )
{
std::vector<double> loss_diffs;
TrainingInstances training_tmp;
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];
p.first_rank = i;
p.second_rank = j;
p.first_score = kb->scores[i];
p.second_score = kb->scores[j];
bool conservative = 1;
if ( kb->scores[i] - kb->scores[j] < 0 ) {
// j=hope, i=fear
p.model_score_diff = kb->model_scores[j] - kb->model_scores[i];
p.loss_diff = kb->scores[j] - kb->scores[i];
training_tmp.push_back(p);
loss_diffs.push_back(p.loss_diff);
}
else if (!conservative) {
// i=hope, j=fear
p.model_score_diff = kb->model_scores[i] - kb->model_scores[j];
p.loss_diff = kb->scores[i] - kb->scores[j];
training_tmp.push_back(p);
loss_diffs.push_back(p.loss_diff);
}
}
}
if (training_tmp.size() > 0) {
double threshold;
std::sort(loss_diffs.begin(), loss_diffs.end());
std::reverse(loss_diffs.begin(), loss_diffs.end());
threshold = loss_diffs.size() >= n_pairs ? loss_diffs[n_pairs-1] : loss_diffs[loss_diffs.size()-1];
cerr << "threshold: " << threshold << endl;
size_t constraints = 0;
for (size_t i = 0; (i < training_tmp.size() && constraints < n_pairs); ++i) {
if (training_tmp[i].loss_diff >= threshold) {
training.push_back(training_tmp[i]);
constraints++;
}
}
}
else {
cerr << "No pairs selected." << endl;
}
}
void
sample_rand( KBestList* kb, TrainingInstances &training )
{
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 ) {
TPair p;
p.first = kb->feats[i];
p.second = kb->feats[j];
p.first_rank = i;
p.second_rank = j;
p.first_score = kb->scores[i];
p.second_score = kb->scores[j];
training.push_back( p );
}
}
}
}
} // namespace
#endif
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