Little optimization

transition_machine/include/FeatureModel.hpp

@@ -108,7 +108,7 @@ class FeatureModel
   /// @param config The Config to transform.
   ///
   /// @return The FeatureDescription of the Config.
-  FeatureDescription getFeatureDescription(Config & config);
+  FeatureDescription & getFeatureDescription(Config & config);
 };
 
 #endif

transition_machine/src/Classifier.cpp

@@ -98,7 +98,7 @@ Classifier::WeightedActions Classifier::weightActions(Config & config)
   {
     initClassifier(config);
 
-    auto fd = fm->getFeatureDescription(config);
+    auto & fd = fm->getFeatureDescription(config);
     auto scores = nn->predict(fd);
 
     if (ProgramParameters::showFeatureRepresentation == 1)
@@ -270,7 +270,7 @@ std::string Classifier::getDefaultAction() const
 
 float Classifier::trainOnExample(Config & config, int gold)
 {
-  auto fd = fm->getFeatureDescription(config);
+  auto & fd = fm->getFeatureDescription(config);
   return nn->update(fd, gold);
 }
 

transition_machine/src/FeatureModel.cpp

@@ -8,14 +8,15 @@ FeatureModel::Feature::Feature(const std::string & name)
   this->func = FeatureBank::str2func(name);
 }
 
-FeatureModel::FeatureDescription FeatureModel::getFeatureDescription(Config & config)
+FeatureModel::FeatureDescription & FeatureModel::getFeatureDescription(Config & config)
 {
-  std::vector<FeatureValue> featureDescription;
+  static FeatureDescription featureDescription;
+  featureDescription.values.clear();
 
   for(auto & feature : features)
-    featureDescription.emplace_back(feature.func(config));
+    featureDescription.values.emplace_back(feature.func(config));
 
-  return {featureDescription};
+  return featureDescription;
 }
 
 FeatureModel::FeatureModel(const std::string & filename)