rm

1 files changed, 0 insertions, 61 deletions

diff --git a/training/dtrain/sample_net.h b/training/dtrain/sample_net.h
deleted file mode 100644
index 497149d9..00000000
--- a/training/dtrain/sample_net.h
+++ /dev/null
@@ -1,61 +0,0 @@
-#ifndef _DTRAIN_SAMPLE_NET_H_
-#define _DTRAIN_SAMPLE_NET_H_
-
-#include "kbest.h"
-
-#include "score.h"
-
-namespace dtrain
-{
-
-struct ScoredKbest : public DecoderObserver
-{
-  const size_t k_;
-  size_t feature_count_, effective_sz_;
-  vector<ScoredHyp> samples_;
-  PerSentenceBleuScorer* scorer_;
-  vector<Ngrams>* ref_ngs_;
-  vector<size_t>* ref_ls_;
-  bool dont_score;
-
-  ScoredKbest(const size_t k, PerSentenceBleuScorer* scorer) :
-    k_(k), scorer_(scorer), dont_score(false) {}
-
-  virtual void
-  NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
-  {
-    samples_.clear(); effective_sz_ = feature_count_ = 0;
-    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,
-      KBest::FilterUnique, prob_t, EdgeProb> kbest(*hg, k_);
-    for (size_t i = 0; i < k_; ++i) {
-      const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,
-            KBest::FilterUnique, prob_t, EdgeProb>::Derivation* d =
-              kbest.LazyKthBest(hg->nodes_.size() - 1, i);
-      if (!d) break;
-      ScoredHyp h;
-      h.w = d->yield;
-      h.f = d->feature_values;
-      h.model = log(d->score);
-      h.rank = i;
-      if (!dont_score)
-        h.gold = scorer_->Score(h.w, *ref_ngs_, *ref_ls_);
-      samples_.push_back(h);
-      effective_sz_++;
-      feature_count_ += h.f.size();
-    }
-  }
-
-  vector<ScoredHyp>* GetSamples() { return &samples_; }
-  inline void SetReference(vector<Ngrams>& ngs, vector<size_t>& ls)
-  {
-    ref_ngs_ = &ngs;
-    ref_ls_ = &ls;
-  }
-  inline size_t GetFeatureCount() { return feature_count_; }
-  inline size_t GetSize() { return effective_sz_; }
-};
-
-} // namespace
-
-#endif
-