 
       vectors = [
         window.plot.createFactor 'interval', TString, intervalData
-        window.plot.createVector 'width', TNumber, widthData, identity
-        window.plot.createVector 'count', TNumber, countData, identity
+        window.plot.createVector 'width', TNumber, widthData
+        window.plot.createVector 'count', TNumber, countData
       ]
 
       window.plot.createFrame 'distribution', vectors, (sequence binCount), null, 


      vectors = [
        window.plot.createFactor 'characteristic', TString, characteristicData
        window.plot.createVector 'count', TNumber, countData, identity
        window.plot.createVector 'percent', TNumber, percentData, identity
      ]

      window.plot.createFrame 'characteristics', vectors, (sequence characteristicData.length), null,

    description: 'Make a prediction'
    icon: 'bolt'

parseNaNs = (array) ->
  for element in array
    if element is 'NaN' then undefined else element

computeTruePositiveRate = (cm) ->
  [[tn, fp], [fn, tp]] = cm
  tp / (tp + fn)
