.predictions
  %table
    %tr
      %th= @title.gsub(/_lazar_.*$/,' ').capitalize
      %th Prediction
      %th 
        %a{:href => "#", :id => "linkConfidence#{p.object_id}"} Confidence
        :javascript
          $("a#linkConfidence#{p.object_id}").click(function () {
            $("dl#confidence").toggle();  
          });
      %th Relevant features
    %tr
      %th
        %img{:src => @compound.image_uri, :alt => @compound.smiles}
        %td
          - if @measured_activities
            %br
            - @measured_activities.each do |a|
              - if activity(a) == 'active'
                .active
                  = activity(a)
              - elsif activity(a) == 'inactive'
                .inactive
                  = activity(a)
              - else
                = a
            %br
              (
              %a{:href => "#", :id => "linkTrainingData#{p.object_id}"} Training data
              :javascript
                $("a#linkTrainingData#{p.object_id}").click(function () {
                  $("dl#training_data").toggle();  
                });
              )

          - else
            - if activity(@activity) == 'active'
              .active
                = activity(@activity)
            - elsif activity(@activity) == 'inactive'
              .inactive
                = activity(@activity)
            - elsif @activity.is_a?(Float)
              .other
                = sprintf('%.03g', @activity)
            - else
              .other
                %em= @activity.to_s
        %td
          = sprintf('%.03g', @confidence.to_f.abs) if @confidence
        %td
          %table
            %tr
              %th{:colspan => 2} activating
              %th p value
            - if @features[:activating]
              - @features[:activating].sort{|a,b| b.last <=> a.last }.each do |f|
                %tr
                  %th= f[0]
                  %td= f[1]
            %tr
              %th{:colspan => 2} deactivating
              %th p value
            - if @features[:deactivating]
              - @features[:deactivating].sort{|a,b| b.last <=> a.last }.each do |f|
                %tr
                  %th= f[0]
                  %td= f[1]
    %tr
      %th Neighbors
      %th Activity
      %th Similarity (activity specific)
      %th Relevant features
    - @neighbors.sort{|a,b| b.last[:similarity] <=> a.last[:similarity]}.each do |uri,data|
      - c = OpenTox::Compound.new(:uri => uri)
      %tr
        %th 
          %br= c.smiles
          %br
            %a{:href => c.image_uri, :target => "_blank"} Image
          %br
            %img{:src => c.image_uri, :alt => c.smiles}
        %td
          - data[:activities].each do |act|
            - if activity(act) == 'active'
              .active
                = activity(act)
            - elsif activity(act) == 'inactive'
              .inactive
                = activity(act)
            - elsif act.is_a?(Float)
              .other
                = sprintf('%.03g', act)
            - else
              .other
                %em= act.to_s
        %td
          = sprintf('%.03g', data[:similarity])
        %td
          %table
            %tr
              %th{:colspan => 2} activating
              %th p value
              -#%td= data[:features].inspect
            -# data[:features][:activating].each do |f|
            - data[:features][:activating].sort{|a,b| b.last[:p_value] <=> a.last[:p_value] }.each do |f|
              -# f.inspect
              %tr
                %th= f[:smarts]
                %td= f[:p_value]
                %td=# f[:p_value]
            %tr
              %th{:colspan => 2} deactivating
              %th p value
            -# data[:features][:deactivating].sort{|a,b| b.last[:p_value] <=> a.last[:p_value] }.each do |f|
            - data[:features][:deactivating].each do |f|
              %tr
                %th= f[:smarts]
                %td= f[:p_value]



%dl#confidence{ :style => "display: none;" }
  %dt Confidence:
  %dd Indicates the applicability domain of a model. Predictions with a high confidence can be expected to be more reliable than predictions with low confidence. Confidence values may take any value between 0 and 1. For most models confidence &gt; 0.025 is a sensible (hard) cutoff to distiguish between reliable and unreliable predictions.


%dl#training_data{ :style => "display: none;" }
  %dt Training data:
  %dd Experimental result(s) from the training dataset are displayed here.