From 7c3bd90c26dfeea2db3cf74a1cefc23d8dece7c0 Mon Sep 17 00:00:00 2001
From: Christoph Helma <helma@in-silico.ch>
Date: Tue, 15 Mar 2016 17:40:40 +0100
Subject: validation tests pass

---
 lib/classification.rb | 73 ---------------------------------------------------
 1 file changed, 73 deletions(-)

(limited to 'lib/classification.rb')

diff --git a/lib/classification.rb b/lib/classification.rb
index abbb5b3..0202940 100644
--- a/lib/classification.rb
+++ b/lib/classification.rb
@@ -28,80 +28,7 @@ module OpenTox
           bad_request_error "Cannot predict more than 2 classes, multinomial classifications is not yet implemented. Received classes were: '#{weighted.sum.keys}'"
         end
       end
-
-      # Classification with majority vote from neighbors weighted by similarity
-      # @param [Hash] params Keys `:activities, :sims, :value_map` are required
-      # @return [Numeric] A prediction value.
-      def self.fminer_weighted_majority_vote neighbors, training_dataset
-
-        neighbor_contribution = 0.0
-        confidence_sum = 0.0
-
-        $logger.debug "Weighted Majority Vote Classification."
-
-        values = neighbors.collect{|n| n[2]}.uniq
-        neighbors.each do |neighbor|
-          i = training_dataset.compound_ids.index n.id
-          neighbor_weight = neighbor[1]
-          activity = values.index(neighbor[2]) + 1 # map values to integers > 1
-          neighbor_contribution += activity * neighbor_weight
-          if values.size == 2 # AM: provide compat to binary classification: 1=>false 2=>true
-            case activity
-            when 1
-              confidence_sum -= neighbor_weight
-            when 2
-              confidence_sum += neighbor_weight
-            end
-          else
-            confidence_sum += neighbor_weight
-          end
-        end
-        if values.size == 2 
-          if confidence_sum >= 0.0
-            prediction = values[1]
-          elsif confidence_sum < 0.0
-            prediction = values[0] 
-          end
-        elsif values.size == 1 # all neighbors have the same value
-          prediction = values[0] 
-        else 
-          prediction = (neighbor_contribution/confidence_sum).round  # AM: new multinomial prediction
-        end 
-
-        confidence = (confidence_sum/neighbors.size).abs 
-        {:value => prediction, :confidence => confidence.abs}
-      end
-
-      # Local support vector regression from neighbors 
-      # @param [Hash] params Keys `:props, :activities, :sims, :min_train_performance` are required
-      # @return [Numeric] A prediction value.
-      def self.local_svm_classification(params)
-
-        confidence = 0.0
-        prediction = nil
-
-        $logger.debug "Local SVM."
-        if params[:activities].size>0
-          if params[:props]
-            n_prop = params[:props][0].collect.to_a
-            q_prop = params[:props][1].collect.to_a
-            props = [ n_prop, q_prop ]
-          end
-          activities = params[:activities].collect.to_a
-          activities = activities.collect{|v| "Val" + v.to_s} # Convert to string for R to recognize classification
-          prediction = local_svm_prop( props, activities, params[:min_train_performance]) # params[:props].nil? signals non-prop setting
-          prediction = prediction.sub(/Val/,"") if prediction # Convert back
-          confidence = 0.0 if prediction.nil?
-          confidence = get_confidence({:sims => params[:sims][1], :activities => params[:activities]})
-        end
-        {:value => prediction, :confidence => confidence}
-
-      end
-
-
-
     end
-
   end
 end
 
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