merge w/ upstream

1 files changed, 0 insertions, 40 deletions

diff --git a/decoder/tromble_loss.h b/decoder/tromble_loss.h
deleted file mode 100644
index fde33100..00000000
--- a/decoder/tromble_loss.h
+++ /dev/null
@@ -1,40 +0,0 @@
-#ifndef _TROMBLE_LOSS_H_
-#define _TROMBLE_LOSS_H_
-
-#include <vector>
-#include <boost/scoped_ptr.hpp>
-#include <boost/utility/base_from_member.hpp>
-
-#include "ff.h"
-#include "wordid.h"
-
-// this may not be the most elegant way to implement this computation, but since we
-// may need cube pruning and state splitting, we reuse the feature detector framework.
-// the loss is then stored in a feature #0 (which is guaranteed to have weight 0 and
-// never be a "real" feature).
-class TrombleLossComputerImpl;
-class TrombleLossComputer : private boost::base_from_member<boost::scoped_ptr<TrombleLossComputerImpl> >, public FeatureFunction {
- private:
-  typedef boost::scoped_ptr<TrombleLossComputerImpl> PImpl;
-  typedef FeatureFunction Base;
-
- public:
-  // String parameters are ref.txt num_ref weight1 weight2 ... weightn
-  // where ref.txt contains references on per line, with num_ref references per sentence
-  // The weights are the weight on each length n-gram.
-  explicit TrombleLossComputer(const std::string &params);
-
-  ~TrombleLossComputer();
-
- protected:
-  virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
-                                     const HG::Edge& edge,
-                                     const std::vector<const void*>& ant_contexts,
-                                     SparseVector<double>* features,
-                                     SparseVector<double>* estimated_features,
-                                     void* out_context) const;
- private:
-  const int fid_;
-};
-
-#endif