kbest, ksampler refactoring

1 files changed, 26 insertions, 29 deletions

diff --git a/dtrain/kbestget.h b/dtrain/kbestget.h
index 79201182..403384de 100644
--- a/dtrain/kbestget.h
+++ b/dtrain/kbestget.h
@@ -7,28 +7,27 @@ namespace dtrain
 {
 
 
-struct Samples
+struct ScoredHyp
 {
-  vector<SparseVector<double> > feats;
-  vector<vector<WordID> > sents;
-  vector<double> model_scores;
-  vector<double> scores;
-  size_t GetSize() { return sents.size(); }
+  vector<WordID> w;
+  SparseVector<double> f;
+  score_t model;
+  score_t score;
 };
 
-struct HypoSampler : public DecoderObserver
+struct HypSampler : public DecoderObserver
 {
-  virtual Samples* GetSamples() {}
+  virtual vector<ScoredHyp>* GetSamples() {}
 };
 
-struct KBestGetter : public HypoSampler
+struct KBestGetter : public HypSampler
 {
   const size_t k_;
-  const string filter_type;
-  Samples s;
+  const string filter_type_;
+  vector<ScoredHyp> s_;
 
   KBestGetter(const size_t k, const string filter_type) :
-    k_(k), filter_type(filter_type) {}
+    k_(k), filter_type_(filter_type) {}
 
   virtual void
   NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
@@ -36,14 +35,14 @@ struct KBestGetter : public HypoSampler
     KBest(*hg);
   }
 
-  Samples* GetSamples() { return &s; }
+  vector<ScoredHyp>* GetSamples() { return &s_; }
 
   void
   KBest(const Hypergraph& forest)
   {
-    if (filter_type == "unique") {
+    if (filter_type_ == "unique") {
       KBestUnique(forest);
-    } else if (filter_type == "no") {
+    } else if (filter_type_ == "no") {
       KBestNoFilter(forest);
     }
   }
@@ -51,36 +50,34 @@ struct KBestGetter : public HypoSampler
   void
   KBestUnique(const Hypergraph& forest)
   {
-    s.sents.clear();
-    s.feats.clear();
-    s.model_scores.clear();
-    s.scores.clear();
+    s_.clear();
     KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique, prob_t, EdgeProb> kbest(forest, k_);
     for (size_t i = 0; i < k_; ++i) {
       const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique, prob_t, EdgeProb>::Derivation* d =
             kbest.LazyKthBest(forest.nodes_.size() - 1, i);
       if (!d) break;
-      s.sents.push_back(d->yield);
-      s.feats.push_back(d->feature_values);
-      s.model_scores.push_back(log(d->score));
+      ScoredHyp h;
+      h.w = d->yield;
+      h.f = d->feature_values;
+      h.model = log(d->score);
+      s_.push_back(h);
     }
   }
 
   void
   KBestNoFilter(const Hypergraph& forest)
   {
-    s.sents.clear();
-    s.feats.clear();
-    s.model_scores.clear();
-    s.scores.clear();
+    s_.clear();
     KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, k_);
     for (size_t i = 0; i < k_; ++i) {
       const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
             kbest.LazyKthBest(forest.nodes_.size() - 1, i);
       if (!d) break;
-      s.sents.push_back(d->yield);
-      s.feats.push_back(d->feature_values);
-      s.model_scores.push_back(log(d->score));
+      ScoredHyp h;
+      h.w = d->yield;
+      h.f = d->feature_values;
+      h.model = log(d->score);
+      s_.push_back(h);
     }
   }
 };