anchor.diff:              |> DSet.mapReduce 
                   (fun (words,label) -> seq { for w in words -> w,label } )
                   (fun (word, labels) -> 
  -                    let histogram : int[] = Array.zeroCreate numLabels
  +                    let histogram : int[] = Array.zeroCreate numClasses
                       for l in labels do
                           histogram.[l] <- histogram.[l] + 1
                       word, histogram)
  
positive.code: 
  open Prajna.Examples.Common
  
  type internal Counts = Dictionary<string, int>
  
  type NaiveBayes() =
  
      let data = Path.Combine(Utility.GetExecutingDir(), "20news-featurized2-tiny.txt")
  
      let split (c: char) (str:string) = str.Split([|c|], StringSplitOptions.RemoveEmptyEntries)
  
negative.code: 
          // Call this to test prediction accuracy on large dataset, but not during unit test
          let evaluate() =
              printfn "Testing..."
  
              // A class' "prior" is simply the probability of the class in the dataset overall,
              // before we look at any words
              let priors: float[] = 
                  let labelCounts = 
                      trainSet
  