Merge branch 'release/v1.0.0'v1.0.0

2 files changed, 26 insertions, 18 deletions

diff --git a/README b/README
index 5182758..8d515d2 100644
--- a/README
+++ b/README
@@ -6,7 +6,8 @@ OpenTox Algorithm
   - fminer
   - lazar
 
-REST operations:
+REST operations
+---------------
 
 Get a list of all algorithms  GET   /       -                       URIs of algorithms        200
 Get a representation of the   GET   /fminer -                       fminer representation     200,404
@@ -19,11 +20,13 @@ Create lazar model            POST  /lazar  dataset_uri,            URI for laza
                                             prediction_feature, 
                                             feature_generation_uri  
 
-Supported MIME formats (http://chemical-mime.sourceforge.net/):
+Supported MIME formats (http://chemical-mime.sourceforge.net/)
+--------------------------------------------------------------
 
   * application/rdf+xml (default): read/write OWL-DL
 
-Examples:
+Examples
+--------
 
 Get the OWL-DL representation of fminer 
   curl http://webservices.in-silico.ch/algorithm/fminer
@@ -33,7 +36,7 @@ Get the OWL-DL representation of lazar
 
 Create fminer features
   curl -X POST -d dataset_uri={datset_uri} -d prediction_feature={feature_uri} http://webservices.in-silico.ch/algorithm/fminer
-  (feaure_uri specifies the dependent variable, e.g. http://www.epa.gov/NCCT/dsstox/CentralFieldDef.html#ActivityOutcome_CPDBAS_Hamster)
+  (feature_uri specifies the dependent variable, e.g. http://www.epa.gov/NCCT/dsstox/CentralFieldDef.html#ActivityOutcome_CPDBAS_Hamster)
 
   Creates a dataset with fminer features (backbone refinement class representatives from supervised graph mining, see http://www.maunz.de/libfminer-doc/). These features can be used e.g. as structural alerts, as descriptors (fingerprints) for prediction models or for similarity calculations.
 
@@ -41,7 +44,10 @@ Create lazar model
   curl -X POST -d dataset_uri={datset_uri} -d prediction_feature={feature_uri} -d feature_generation_uri=http://webservices.in-silico.ch/algorithm/fminer http://webservices.in-silico.ch/test/algorithm/lazar
   (feaure_uri specifies the dependent variable, e.g. http://www.epa.gov/NCCT/dsstox/CentralFieldDef.html#ActivityOutcome_CPDBAS_Hamster)
 
-More documentation: Source code (application.rb, fminer.rb, lazar.rb)
+API documentation
+-----------------
+
+http://rdoc.info/github/opentox/algorithm
 
-Copyright (c) 2009 Christoph Helma. See LICENSE for details.
+Copyright (c) 2009-2011 Christoph Helma, Martin Guetlein, Micha Rautenberg, Andreas Maunz, David Vorgrimmler, Denis Gebele. See LICENSE for details.
 
diff --git a/lazar.rb b/lazar.rb
index b80235e..e89d692 100644
--- a/lazar.rb
+++ b/lazar.rb
@@ -113,18 +113,20 @@ post '/lazar/?' do
       end
       
 			lazar.activities[compound] = [] unless lazar.activities[compound]
-      training_activities.data_entries[compound][params[:prediction_feature]].each do |value|
-				case value.to_s
-				when "true"
-					lazar.activities[compound] << true
-				when "false"
-					lazar.activities[compound] << false
-				else 
-          halt 404, "0 values not allowed in training dataset. log10 is calculated internally." if value.to_f == 0
-					lazar.activities[compound] << value.to_f
-          lazar.prediction_algorithm = "Neighbors.local_svm_regression"
-				end
-			end
+      unless training_activities.data_entries[compound][params[:prediction_feature]].empty?
+        training_activities.data_entries[compound][params[:prediction_feature]].each do |value|
+          case value.to_s
+          when "true"
+            lazar.activities[compound] << true
+          when "false"
+            lazar.activities[compound] << false
+          else 
+            halt 404, "0 values not allowed in training dataset. log10 is calculated internally." if value.to_f == 0
+            lazar.activities[compound] << value.to_f
+            lazar.prediction_algorithm = "Neighbors.local_svm_regression"
+          end
+        end
+      end
     end
 
     lazar.metadata[DC.title] = "lazar model for #{URI.decode(File.basename(prediction_feature))}"