removed some quirks, less boost, prettier code, score_t

1 files changed, 39 insertions, 45 deletions

diff --git a/dtrain/kbestget.h b/dtrain/kbestget.h
index cf466fe4..79201182 100644
--- a/dtrain/kbestget.h
+++ b/dtrain/kbestget.h
@@ -1,19 +1,14 @@
 #ifndef _DTRAIN_KBESTGET_H_
 #define _DTRAIN_KBESTGET_H_
 
-
 #include "kbest.h"
 
-
 namespace dtrain
 {
 
 
-/*
- * KBestList
- *
- */
-struct KBestList {
+struct Samples
+{
   vector<SparseVector<double> > feats;
   vector<vector<WordID> > sents;
   vector<double> model_scores;
@@ -21,71 +16,71 @@ struct KBestList {
   size_t GetSize() { return sents.size(); }
 };
 
+struct HypoSampler : public DecoderObserver
+{
+  virtual Samples* GetSamples() {}
+};
 
-/*
- * KBestGetter
- *
- */
-struct KBestGetter : public DecoderObserver
+struct KBestGetter : public HypoSampler
 {
   const size_t k_;
   const string filter_type;
-  KBestList kb;
+  Samples s;
 
-  KBestGetter( const size_t k, const string filter_type ) :
+  KBestGetter(const size_t k, const string filter_type) :
     k_(k), filter_type(filter_type) {}
 
   virtual void
-  NotifyTranslationForest( const SentenceMetadata& smeta, Hypergraph* hg )
+  NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
   {
-    KBest( *hg );
+    KBest(*hg);
   }
 
-  KBestList* GetKBest() { return &kb; }
+  Samples* GetSamples() { return &s; }
 
   void
-  KBest( const Hypergraph& forest )
+  KBest(const Hypergraph& forest)
   {
-    if ( filter_type == "unique" ) {
-      KBestUnique( forest );
-    } else if ( filter_type == "no" ) {
-      KBestNoFilter( forest );
+    if (filter_type == "unique") {
+      KBestUnique(forest);
+    } else if (filter_type == "no") {
+      KBestNoFilter(forest);
     }
   }
 
   void
-  KBestUnique( const Hypergraph& forest )
+  KBestUnique(const Hypergraph& forest)
   {
-    kb.sents.clear();
-    kb.feats.clear();
-    kb.model_scores.clear();
-    kb.scores.clear();
-    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique, prob_t, EdgeProb> kbest( forest, k_ );
-    for ( size_t i = 0; i < k_; ++i ) {
+    s.sents.clear();
+    s.feats.clear();
+    s.model_scores.clear();
+    s.scores.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 );
+            kbest.LazyKthBest(forest.nodes_.size() - 1, i);
       if (!d) break;
-      kb.sents.push_back( d->yield);
-      kb.feats.push_back( d->feature_values );
-      kb.model_scores.push_back( log(d->score) );
+      s.sents.push_back(d->yield);
+      s.feats.push_back(d->feature_values);
+      s.model_scores.push_back(log(d->score));
     }
   }
 
   void
-  KBestNoFilter( const Hypergraph& forest )
+  KBestNoFilter(const Hypergraph& forest)
   {
-    kb.sents.clear();
-    kb.feats.clear();
-    kb.model_scores.clear();
-    kb.scores.clear();
-    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest( forest, k_ );
-    for ( size_t i = 0; i < k_; ++i ) {
+    s.sents.clear();
+    s.feats.clear();
+    s.model_scores.clear();
+    s.scores.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 );
+            kbest.LazyKthBest(forest.nodes_.size() - 1, i);
       if (!d) break;
-      kb.sents.push_back( d->yield);
-      kb.feats.push_back( d->feature_values );
-      kb.model_scores.push_back( log(d->score) );
+      s.sents.push_back(d->yield);
+      s.feats.push_back(d->feature_values);
+      s.model_scores.push_back(log(d->score));
     }
   }
 };
@@ -93,6 +88,5 @@ struct KBestGetter : public DecoderObserver
 
 } // namespace
 
-
 #endif