1 files changed, 0 insertions, 101 deletions
diff --git a/dtrain/test/mtm11/mira_update/sample.h b/dtrain/test/mtm11/mira_update/sample.h
deleted file mode 100644
index 5c331bba..00000000
--- a/dtrain/test/mtm11/mira_update/sample.h
+++ /dev/null
@@ -1,101 +0,0 @@
-#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
-