Merge branch 'development' of github.com:opentox/algorithm into development

2 files changed, 12 insertions, 4 deletions

diff --git a/Rakefile b/Rakefile
index e60ffc7..4a1f25e 100644
--- a/Rakefile
+++ b/Rakefile
@@ -41,6 +41,7 @@ namespace "fminer" do
   task :update do
     puts `git submodule update --init`
     Dir.chdir('libfminer/libbbrc')
+    puts `git checkout master`
     puts `git checkout Makefile`
     puts `git pull`
     puts `./configure`
@@ -52,6 +53,7 @@ namespace "fminer" do
     end
     puts `make ruby`
     Dir.chdir('../liblast')
+    puts `git checkout master`
     puts `git checkout Makefile`
     puts `git pull`
     puts `./configure`
diff --git a/fminer.rb b/fminer.rb
index c37dc93..8654c25 100644
--- a/fminer.rb
+++ b/fminer.rb
@@ -74,8 +74,11 @@ post '/fminer/bbrc/?' do
   training_dataset = OpenTox::Dataset.find "#{params[:dataset_uri]}", @subjectid
   halt 404, "No feature #{params[:prediction_feature]} in dataset #{params[:dataset_uri]}" unless training_dataset.features and training_dataset.features.include?(params[:prediction_feature])
 
-  unless minfreq = params[:min_frequency]
-    minfreq = 5*training_dataset.compounds.size/1000 # 8 promille according to Andreas suggestions
+  unless params[:min_frequency].nil? 
+    minfreq=params[:min_frequency].to_i
+    raise "Minimum frequency must be a number >0!" unless minfreq>0
+  else
+    minfreq = 5*training_dataset.compounds.size/1000 # AM sugg. 8-10 per mil
     minfreq = 2 unless minfreq > 2
   end
 
@@ -247,8 +250,11 @@ post '/fminer/last/?' do
   training_dataset.load_all(@subjectid)
   halt 404, "No feature #{params[:prediction_feature]} in dataset #{params[:dataset_uri]}" unless training_dataset.features and training_dataset.features.include?(params[:prediction_feature])
 
-  unless minfreq = params[:min_frequency]
-    minfreq = 8*training_dataset.compounds.size/100 # 8% according to Andreas suggestions
+  unless params[:min_frequency].nil? 
+    minfreq=params[:min_frequency].to_i
+    raise "Minimum frequency must be a number >0!" unless minfreq>0
+  else
+    minfreq = 8*training_dataset.compounds.size/100 # AM sugg. 5-10%
     minfreq = 2 unless minfreq > 2
   end