:javascript $(document).ready(function(){ $('[data-toggle="popover"]').popover(); $('.modal').on('hidden.bs.modal', function () { $(this).removeData('bs.modal'); }); }); %div.well %a.btn.btn-warning{:href => to('/predict')} %i.glyphicon.glyphicon-menu-left New Prediction / displays all prediction result in first table %h3 Prediction Results: %div.table-responsive %table.table.table-bordered{:id=>"overview", :style=>"background-color:white;"} %tbody %tr %td{:id=>"compound", :style=>"vertical-align:top;"} %p= @compound.svg %p= @compound.smiles - mw = @compound.molecular_weight - @model_types = {} - @dbhit = {} - @predictions.each_with_index do |prediction,i| - type = @models[i].model.class.to_s.match("Classification") ? "Classification" : "Regression" - @model_types[i] = type %td{:style=>"vertical-align:top;white-space:nowrap;"} %b{:class => "title"} = "#{@models[i].endpoint.gsub('_', ' ')} (#{@models[i].species})" %p - if prediction[:confidence] == "measured" - @dbhit[i] = true %p %b Measured activity: - p prediction[:value] - if prediction[:value].is_a?(Array) = (type == "Regression") ? prediction[:value].collect{|v| weight = Compound.from_smiles(@compound.smiles).mmol_to_mg(v); '%.2e' % v + " (#{@models[i].unit})"+"|#{'%.2e' % weight} (mg/kg_bw/day)"}.join("") : prediction[:value].join(", ") - else = (type == "Regression") ? "#{"%.2e" % prediction[:value]} (#{@models[i].unit}) | #{'%.2e' % @compound.mmol_to_mg(prediction[:value])} (mg/kg_bw/day)" : prediction[:value] %p %b Compound is part of the training dataset - elsif prediction[:neighbors].size > 0 %p / model type (classification|regression) %b Type: = type %br %b Prediction: = (type == "Regression") ? "#{'%.2e' % prediction[:value]} (#{@models[i].unit}) | #{'%.2e' % @compound.mmol_to_mg(prediction[:value])} (mg/kg_bw/day)" : prediction[:value] / TODO update description / %a.btn.glyphicon.glyphicon-info-sign{:href=>"#", :title=>"Prediction", data: {toggle:"popover", placement:"left", html:"true", content:"LAZAR calculates searches the training dataset for similar compounds (neighbors) and calculates the prediction from their measured activities. LAZAR calculates predictions using