Set nr_hits for regression as default. Minor modifications.

2 files changed, 14 insertions, 5 deletions

diff --git a/fminer.rb b/fminer.rb
index e220b09..d06f7ac 100644
--- a/fminer.rb
+++ b/fminer.rb
@@ -193,7 +193,7 @@ post '/fminer/bbrc/?' do
         id_arrs.each { |id_count_hash|
           id=id_count_hash.keys[0].to_i
           count=id_count_hash.values[0].to_i
-          if params[:nr_hits] == "true"
+          if params[:nr_hits]
             feature_dataset.add(fminer.compounds[id], feature_uri, count)
           else
             feature_dataset.add(fminer.compounds[id], feature_uri, 1)
@@ -281,7 +281,7 @@ post '/fminer/last/?' do
     lu = LU.new                             # AM LAST: uses last-utils here
     dom=lu.read(xml)                        # AM LAST: parse GraphML 
     smarts=lu.smarts_rb(dom,'nls')          # AM LAST: converts patterns to LAST-SMARTS using msa variant (see last-pm.maunz.de)
-    params[:nr_hits] != "true" ? hit_count=false: hit_count=true
+    params[:nr_hits] ? hit_count=true: hit_count=false
     matches, counts = lu.match_rb(fminer.smi,smarts,hit_count)       # AM LAST: creates instantiations
 
     matches.each do |smarts, ids|
@@ -314,7 +314,7 @@ post '/fminer/last/?' do
         feature_dataset.add_feature feature_uri, metadata
       end
       if !hit_count
-        ids.each { |id| feature_dataset.add(fminer.compounds[id], feature_uri, true)}
+        ids.each { |id| feature_dataset.add(fminer.compounds[id], feature_uri, 1)}
       else
         ids.each_with_index { |id,i| feature_dataset.add(fminer.compounds[id], feature_uri, counts[smarts][i])} 
       end
diff --git a/lazar.rb b/lazar.rb
index 5de3790..c210f69 100644
--- a/lazar.rb
+++ b/lazar.rb
@@ -61,8 +61,7 @@ post '/lazar/?' do
 
 		lazar = OpenTox::Model::Lazar.new
     lazar.min_sim = params[:min_sim].to_f if params[:min_sim]
-    lazar.nr_hits = true if params[:nr_hits] == "true"
-
+    
     if prediction_feature.feature_type == "classification"
       @training_classes = training_activities.accept_values(prediction_feature.uri).sort
       @training_classes.each_with_index { |c,i|
@@ -70,8 +69,18 @@ post '/lazar/?' do
         params[:value_map] = lazar.value_map
       }
     elsif  prediction_feature.feature_type == "regression"
+      lazar.nr_hits = true
       lazar.prediction_algorithm = "Neighbors.local_svm_regression" 
     end
+
+    if params[:nr_hits] == "false" # if nr_hits is set optional to true/false it will return as String (but should be True/FalseClass)
+      lazar.nr_hits = false
+      params[:nr_hits] = false
+    elsif params[:nr_hits] == "true"
+      lazar.nr_hits = true
+    end
+    params[:nr_hits] = true if lazar.nr_hits
+
     task.progress 10
 
 		if params[:feature_dataset_uri]