perceptron

gistfile1.scala

object Perceptron {
  type Vec = Map[Symbol, Double]

  def makeVec(R: Double, G: Double, B: Double, bias: Double): Vec =
    Map('R -> R, 'G -> G, 'B -> B, 'bias -> bias)

  def predict(w: Vec, x: Vec): Double =
    w.keys.filter(w(_) != 0).map(k => w(k) * x(k)).sum

  def train(w: Vec, x: Vec, t: Double): Vec =
    if (predict(w, x) * t < 0)
      x.map{case (k, v) => (k, w(k) + t * x(k))}
    else
      w

  val xs: List[Vec] =
    List(Map('R -> 255, 'G ->   0, 'B ->   0, 'bias -> 1),
         Map('R ->   0, 'G -> 255, 'B -> 255, 'bias -> 1),
         Map('R ->   0, 'G -> 255, 'B ->   0, 'bias -> 1),
         Map('R -> 255, 'G ->   0, 'B -> 255, 'bias -> 1),
         Map('R ->   0, 'G ->   0, 'B -> 255, 'bias -> 1),
         Map('R -> 255, 'G -> 255, 'B ->   0, 'bias -> 1))

  val ts: List[Double] = List(1, -1, -1, 1, -1, 1)
  
  val w0: Vec = Map('R -> 0, 'G -> 0, 'B -> 0, 'bias -> 1)

  val trainingSet = Iterator.continually(xs zip ts).take(10).flatten

  val wn = trainingSet.foldLeft(w0){
    case (w, (x, t)) => train(w, x, t)
  }
}