dtrain: rm 'check' and ksampler

1 files changed, 0 insertions, 88 deletions

diff --git a/training/dtrain/kbestget.h b/training/dtrain/kbestget.h
deleted file mode 100644
index 25f02273..00000000
--- a/training/dtrain/kbestget.h
+++ /dev/null
@@ -1,88 +0,0 @@
-#ifndef _DTRAIN_KBESTGET_H_
-#define _DTRAIN_KBESTGET_H_
-
-#include "kbest.h"
-
-namespace dtrain
-{
-
-
-struct KBestGetter : public HypSampler
-{
-  const unsigned k_;
-  const string filter_type_;
-  vector<ScoredHyp> s_;
-  unsigned src_len_;
-
-  KBestGetter(const unsigned k, const string filter_type) :
-    k_(k), filter_type_(filter_type) {}
-
-  virtual void
-  NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
-  {
-    src_len_ = smeta.GetSourceLength();
-    KBestScored(*hg);
-  }
-
-  vector<ScoredHyp>* GetSamples() { return &s_; }
-
-  void
-  KBestScored(const Hypergraph& forest)
-  {
-    if (filter_type_ == "uniq") {
-      KBestUnique(forest);
-    } else if (filter_type_ == "not") {
-      KBestNoFilter(forest);
-    }
-  }
-
-  void
-  KBestUnique(const Hypergraph& forest)
-  {
-    s_.clear(); sz_ = f_count_ = 0;
-    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,
-      KBest::FilterUnique, prob_t, EdgeProb> kbest(forest, k_);
-    for (unsigned 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;
-      ScoredHyp h;
-      h.w = d->yield;
-      h.f = d->feature_values;
-      h.model = log(d->score);
-      h.rank = i;
-      h.score = scorer_->Score(h.w, *refs_, i, src_len_);
-      s_.push_back(h);
-      sz_++;
-      f_count_ += h.f.size();
-    }
-  }
-
-  void
-  KBestNoFilter(const Hypergraph& forest)
-  {
-    s_.clear(); sz_ = f_count_ = 0;
-    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, k_);
-    for (unsigned i = 0; i < k_; ++i) {
-      const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
-            kbest.LazyKthBest(forest.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;
-      h.score = scorer_->Score(h.w, *refs_, i, src_len_);
-      s_.push_back(h);
-      sz_++;
-      f_count_ += h.f.size();
-    }
-  }
-};
-
-
-} // namespace
-
-#endif
-