From b20c6665542cbfa1b4328b349d6912944a1483f2 Mon Sep 17 00:00:00 2001
From: Patrick Simianer <p@simianer.de>
Date: Thu, 26 Feb 2015 22:12:23 +0100
Subject: last tweaks & fixes

---
 training/dtrain/sample.h | 31 ++++++++++++++-----------------
 1 file changed, 14 insertions(+), 17 deletions(-)

(limited to 'training/dtrain/sample.h')

diff --git a/training/dtrain/sample.h b/training/dtrain/sample.h
index c3586c58..03cc82c3 100644
--- a/training/dtrain/sample.h
+++ b/training/dtrain/sample.h
@@ -3,20 +3,19 @@
 
 #include "kbest.h"
 
+#include "score.h"
+
 namespace dtrain
 {
 
-
 struct ScoredKbest : public DecoderObserver
 {
   const size_t k_;
-  vector<ScoredHyp> s_;
-  size_t src_len_;
+  size_t feature_count_, effective_sz_;
+  vector<ScoredHyp> samples_;
   PerSentenceBleuScorer* scorer_;
-  vector<vector<WordID> >* refs_;
   vector<Ngrams>* ref_ngs_;
   vector<size_t>* ref_ls_;
-  size_t f_count_, sz_;
 
   ScoredKbest(const size_t k, PerSentenceBleuScorer* scorer) :
     k_(k), scorer_(scorer) {}
@@ -24,14 +23,13 @@ struct ScoredKbest : public DecoderObserver
   virtual void
   NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
   {
-    src_len_ = smeta.GetSourceLength();
-    s_.clear(); sz_ = f_count_ = 0;
+    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);
+      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;
@@ -39,23 +37,22 @@ struct ScoredKbest : public DecoderObserver
       h.model = log(d->score);
       h.rank = i;
       h.gold = scorer_->Score(h.w, *ref_ngs_, *ref_ls_);
-      s_.push_back(h);
-      sz_++;
-      f_count_ += h.f.size();
+      samples_.push_back(h);
+      effective_sz_++;
+      feature_count_ += h.f.size();
     }
   }
 
-  vector<ScoredHyp>* GetSamples() { return &s_; }
+  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 f_count_; }
-  inline size_t GetSize() { return sz_; }
+  inline size_t GetFeatureCount() { return feature_count_; }
+  inline size_t GetSize() { return effective_sz_; }
 };
 
-
 } // namespace
 
 #endif
-- 
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