fixed output, removed obsolete files

1 files changed, 101 insertions, 0 deletions

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