initial checkin

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@2 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 40 insertions, 0 deletions

diff --git a/decoder/tromble_loss.h b/decoder/tromble_loss.h
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+++ b/decoder/tromble_loss.h
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+#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 Hypergraph::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