def traverse_classification_forest
      @predictions = {}
      @options.read_testing_data{|individual|
        individual_prediction = []
        trees.each do |t|
          individual_prediction << Nimbus::Tree.traverse(t, individual.snp_list)
        end
        class_sizes = Nimbus::LossFunctions.class_sizes_in_list(individual_prediction, @options.tree[:classes]).map{|p| (p/individual_prediction.size.to_f).round(3)}
        @predictions[individual.id] = Hash[@options.tree[:classes].zip class_sizes].map{|k,v| "'#{k}': #{v}"}.join(' , ')
      }
    end
def traverse_classification_forest
      @predictions = {}
      @options.read_testing_data{|individual|
        individual_prediction = []
        trees.each do |t|
          individual_prediction << Nimbus::Tree.traverse(t, individual.snp_list)
        end
        class_sizes = Nimbus::LossFunctions.class_sizes_in_list(individual_prediction, @options.tree[:classes]).map{|p| (p/individual_prediction.size.to_f).round(3)}
        @predictions[individual.id] = Hash[@options.tree[:classes].zip class_sizes].map{|k,v| "'#{k}': #{v}"}.join(' , ')
      }
    end
Traverse a testing set through every classification tree of the forest and get majority class predictions for every individual in the sample.