decodeFn extract

gistfile1.scala

    class RegionVar(val score: Int) extends IntegerVariable(score)

    object PixelDomain extends DiscreteDomain(numClasses)

    class Pixel(i: Int) extends DiscreteVariable(i) { //i is just the initial value 
      def domain = PixelDomain
    }

    val labelParams = Array.fill(graph.size) { new Pixel(0) }
    val nodePairsUsed: HashSet[(Int, Int)] = new HashSet[(Int, Int)]()
    for (idx <- 0 until labelParams.length) {
      model ++= new Factor1(labelParams(idx)) {
        def score(k: Pixel#Value) = thetaUnary(idx, k.intValue)
      }

      if (!DISABLE_PAIRWISE) {

        graph.getC(idx).foreach { neighbour =>
          {
            if (!nodePairsUsed.contains((idx, neighbour)) && !nodePairsUsed.contains((neighbour, idx))) { //This prevents adding neighbours twice 
              nodePairsUsed.add((idx, neighbour))
              model ++= new Factor2(labelParams(idx), labelParams(neighbour)) {
                def score(i: Pixel#Value, j: Pixel#Value) = thetaPairwise(i.intValue, j.intValue)
              }
            }
          }
        }
      }
    }

   
    if(counter<1) println("nodePairsFound" +nodePairsUsed.size+" Input thetaUnary("+thetaUnary.rows+","+thetaUnary.cols+")/nFactor Graph Size: "+model.factors.size)//TODO remove 
 

Note that thetaUnary are the unary potentials and thetaPairwise are the pairwise potentials which we are learning on the outside via the Weight vector