major restructure of the training code

1 files changed, 0 insertions, 61 deletions

diff --git a/dtrain/ksampler.h b/dtrain/ksampler.h
deleted file mode 100644
index bc2f56cd..00000000
--- a/dtrain/ksampler.h
+++ /dev/null
@@ -1,61 +0,0 @@
-#ifndef _DTRAIN_KSAMPLER_H_
-#define _DTRAIN_KSAMPLER_H_
-
-#include "hg_sampler.h" // cdec
-#include "kbestget.h"
-#include "score.h"
-
-namespace dtrain
-{
-
-bool
-cmp_hyp_by_model_d(ScoredHyp a, ScoredHyp b)
-{
-  return a.model > b.model;
-}
-
-struct KSampler : public HypSampler
-{
-  const unsigned k_;
-  vector<ScoredHyp> s_;
-  MT19937* prng_;
-  score_t (*scorer)(NgramCounts&, const unsigned, const unsigned, unsigned, vector<score_t>);
-  unsigned src_len_;
-
-  explicit KSampler(const unsigned k, MT19937* prng) :
-    k_(k), prng_(prng) {}
-
-  virtual void
-  NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
-  {
-    src_len_ = smeta.GetSourceLength();
-    ScoredSamples(*hg);
-  }
-
-  vector<ScoredHyp>* GetSamples() { return &s_; }
-
-  void ScoredSamples(const Hypergraph& forest) {
-    s_.clear(); sz_ = f_count_ = 0;
-    std::vector<HypergraphSampler::Hypothesis> samples;
-    HypergraphSampler::sample_hypotheses(forest, k_, prng_, &samples);
-    for (unsigned i = 0; i < k_; ++i) {
-      ScoredHyp h;
-      h.w = samples[i].words;
-      h.f = samples[i].fmap;
-      h.model = log(samples[i].model_score);
-      h.rank = i;
-      h.score = scorer_->Score(h.w, *ref_, i, src_len_);
-      s_.push_back(h);
-      sz_++;
-      f_count_ += h.f.size();
-    }
-    sort(s_.begin(), s_.end(), cmp_hyp_by_model_d);
-    for (unsigned i = 0; i < s_.size(); i++) s_[i].rank = i;
-  }
-};
-
-
-} // namespace
-
-#endif
-