1 files changed, 14 insertions, 23 deletions
diff --git a/rst_parser/mst_train.cc b/rst_parser/mst_train.cc
index f0403d7e..0709e7c9 100644
--- a/rst_parser/mst_train.cc
+++ b/rst_parser/mst_train.cc
@@ -6,7 +6,6 @@
 #include <boost/program_options/variables_map.hpp>
 
 #include "arc_ff.h"
-#include "arc_ff_factory.h"
 #include "stringlib.h"
 #include "filelib.h"
 #include "tdict.h"
@@ -22,7 +21,6 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   string cfg_file;
   opts.add_options()
         ("training_data,t",po::value<string>()->default_value("-"), "File containing training data (jsent format)")
-        ("feature_function,F",po::value<vector<string> >()->composing(), "feature function (multiple permitted)")
         ("weights,w",po::value<string>(), "Optional starting weights")
         ("output_every_i_iterations,I",po::value<unsigned>()->default_value(1), "Write weights every I iterations")
         ("regularization_strength,C",po::value<double>()->default_value(1.0), "Regularization strength")
@@ -74,12 +72,8 @@ int main(int argc, char** argv) {
   int size = 1;
   po::variables_map conf;
   InitCommandLine(argc, argv, &conf);
-  ArcFactoredForest af(5);
-  ArcFFRegistry reg;
-  reg.Register("DistancePenalty", new ArcFFFactory<DistancePenalty>);
+  ArcFeatureFunctions ffs;
   vector<TrainingInstance> corpus;
-  vector<boost::shared_ptr<ArcFeatureFunction> > ffs;
-  ffs.push_back(boost::shared_ptr<ArcFeatureFunction>(new DistancePenalty("")));
   TrainingInstance::ReadTraining(conf["training_data"].as<string>(), &corpus, rank, size);
   vector<ArcFactoredForest> forests(corpus.size());
   SparseVector<double> empirical;
@@ -88,22 +82,19 @@ int main(int argc, char** argv) {
     TrainingInstance& cur = corpus[i];
     if (rank == 0 && (i+1) % 10 == 0) { cerr << '.' << flush; flag = true; }
     if (rank == 0 && (i+1) % 400 == 0) { cerr << " [" << (i+1) << "]\n"; flag = false; }
-    for (int fi = 0; fi < ffs.size(); ++fi) {
-      ArcFeatureFunction& ff = *ffs[fi];
-      ff.PrepareForInput(cur.ts);
-      SparseVector<weight_t> efmap;
-      for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
-        efmap.clear();
-        ff.EgdeFeatures(cur.ts, cur.tree.h_m_pairs[j].first,
-                        cur.tree.h_m_pairs[j].second,
-                        &efmap);
-        cur.features += efmap;
-      }
-      for (int j = 0; j < cur.tree.roots.size(); ++j) {
-        efmap.clear();
-        ff.EgdeFeatures(cur.ts, -1, cur.tree.roots[j], &efmap);
-        cur.features += efmap;
-      }
+    ffs.PrepareForInput(cur.ts);
+    SparseVector<weight_t> efmap;
+    for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
+      efmap.clear();
+      ffs.EdgeFeatures(cur.ts, cur.tree.h_m_pairs[j].first,
+                       cur.tree.h_m_pairs[j].second,
+                       &efmap);
+      cur.features += efmap;
+    }
+    for (int j = 0; j < cur.tree.roots.size(); ++j) {
+      efmap.clear();
+      ffs.EdgeFeatures(cur.ts, -1, cur.tree.roots[j], &efmap);
+      cur.features += efmap;
     }
     empirical += cur.features;
     forests[i].resize(cur.ts.words.size());