Merge branch 'master' of github.com:redpony/cdec

1 files changed, 12 insertions, 3 deletions

diff --git a/rst_parser/mst_train.cc b/rst_parser/mst_train.cc
index b3711aba..6332693e 100644
--- a/rst_parser/mst_train.cc
+++ b/rst_parser/mst_train.cc
@@ -28,6 +28,9 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
         ("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")
+#ifdef HAVE_CMPH
+        ("cmph_perfect_feature_hash,h", po::value<string>(), "Load perfect hash function for features")
+#endif
 #if HAVE_THREAD
         ("threads,T",po::value<unsigned>()->default_value(1), "Number of threads")
 #endif
@@ -119,11 +122,19 @@ int main(int argc, char** argv) {
   int size = 1;
   po::variables_map conf;
   InitCommandLine(argc, argv, &conf);
+  if (conf.count("cmph_perfect_feature_hash")) {
+    cerr << "Loading perfect hash function from " << conf["cmph_perfect_feature_hash"].as<string>() << " ...\n";
+    FD::EnableHash(conf["cmph_perfect_feature_hash"].as<string>());
+    cerr << "  " << FD::NumFeats() << " features in map\n";
+  }
   ArcFeatureFunctions ffs;
   vector<TrainingInstance> corpus;
   TrainingInstance::ReadTrainingCorpus(conf["training_data"].as<string>(), &corpus, rank, size);
+  vector<weight_t> weights;
+    Weights::InitFromFile(conf["weights"].as<string>(), &weights);
   vector<ArcFactoredForest> forests(corpus.size());
   SparseVector<double> empirical;
+  cerr << "Extracting features...\n";
   bool flag = false;
   for (int i = 0; i < corpus.size(); ++i) {
     TrainingInstance& cur = corpus[i];
@@ -149,9 +160,7 @@ int main(int argc, char** argv) {
   }
   if (flag) cerr << endl;
   //cerr << "EMP: " << empirical << endl; //DE
-  vector<weight_t> weights(FD::NumFeats(), 0.0);
-  if (conf.count("weights"))
-    Weights::InitFromFile(conf["weights"].as<string>(), &weights);
+  weights.resize(FD::NumFeats(), 0.0);
   vector<weight_t> g(FD::NumFeats(), 0.0);
   cerr << "features initialized\noptimizing...\n";
   boost::shared_ptr<BatchOptimizer> o;