Added common value_map routine to wrapper

2 files changed, 3 insertions, 10 deletions

diff --git a/fminer.rb b/fminer.rb
index 3130d31..ee504fc 100644
--- a/fminer.rb
+++ b/fminer.rb
@@ -139,9 +139,7 @@ post '/fminer/bbrc/?' do
     else
       raise "no accept values for dataset '"+fminer.training_dataset.uri.to_s+"' and feature '"+fminer.prediction_feature.uri.to_s+
         "'" unless fminer.training_dataset.accept_values(fminer.prediction_feature.uri)
-      @training_classes = fminer.training_dataset.accept_values(fminer.prediction_feature.uri).sort
-      @value_map=Hash.new
-      @training_classes.each_with_index { |c,i| @value_map[i+1] = c }
+      @value_map=fminer.training_dataset.value_map(fminer.prediction_feature.uri)
     end
     @@bbrc.SetMinfreq(fminer.minfreq)
     @@bbrc.SetType(1) if params[:feature_type] == "paths"
@@ -270,9 +268,7 @@ post '/fminer/last/?' do
     else
       raise "no accept values for dataset '"+fminer.training_dataset.uri.to_s+"' and feature '"+fminer.prediction_feature.uri.to_s+
         "'" unless fminer.training_dataset.accept_values(fminer.prediction_feature.uri)
-      @training_classes = fminer.training_dataset.accept_values(fminer.prediction_feature.uri).sort
-      @value_map=Hash.new
-      @training_classes.each_with_index { |c,i| @value_map[i+1] = c }
+      @value_map=fminer.training_dataset.value_map(fminer.prediction_feature.uri)
     end
     @@last.SetMinfreq(fminer.minfreq)
     @@last.SetType(1) if params[:feature_type] == "paths"
diff --git a/lazar.rb b/lazar.rb
index a12fa18..61c2485 100644
--- a/lazar.rb
+++ b/lazar.rb
@@ -73,10 +73,7 @@ post '/lazar/?' do
     # Default Values
     # Classification: Weighted Majority, Substructure.match
     if prediction_feature.feature_type == "classification"
-      @training_classes = training_activities.accept_values(prediction_feature.uri).sort
-      @training_classes.each_with_index { |c,i|
-        lazar.value_map[i+1] = c # don't use '0': we must take the weighted mean later.
-      }
+      lazar.value_map = training_activities.value_map(params[:prediction_feature])
     # Regression: SVM, Substructure.match_hits
     elsif  prediction_feature.feature_type == "regression"
       lazar.feature_calculation_algorithm = "Substructure.match_hits"