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object ClassicRunner {
  def run() = {
    val input = Array(
      Array( 0.0, 0.0 ),
      Array( 1.0, 0.0 ),
      Array( 0.0, 1.0 ),
      Array( 1.0, 1.0 )
    )

    val ideal = Array(

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def run() = {

    val input =
      ( 0.0 | 0.0 ) \\
      ( 1.0 | 0.0 ) \\
      ( 0.0 | 1.0 ) \\
      ( 1.0 | 1.0 )

    val ideal =
      Tuple1(0.0) \\

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val b =  (1.0 | 2.0 | 3.0) \\
         (4.0 | 5.0 | 6.0) \\
         (7.0 | 8.0 | 9.0)  
    
    //gives
    // 1.0 2.0 3.0
    // 4.0 5.0 6.0
    // 7.0 8.0 9.0

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implicit class EncogArray1[T](val elem1: T) {
    def |(v: T) = new EncogArray2[T](elem1, v)
  }
  case class EncogArray2[T](elem1: T, elem2: T) {
    def |(v: T) = new EncogArray3[T](elem1, elem2, v)
  }
  case class EncogArray3[T](elem1: T, elem2: T, elem3: T) {
    def |(v: T) = new EncogArray4[T](elem1, elem2, elem3, v)
  }
  case class EncogArray4[T](elem1: T, elem2: T, elem3: T, elem4: T) {

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implicit class EncogArray1[T](val elem1: T) {
    def |(v: T) = new EncogArray2[T](elem1, v)
  }
  case class EncogArray2[T](elem1: T, elem2: T) {
    def |(v: T) = new EncogArray3[T](elem1, elem2, v)
  }
  case class EncogArray3[T](elem1: T, elem2: T, elem3: T) {
    def |(v: T) = new EncogArray4[T](elem1, elem2, elem3, v)
  }
  case class EncogArray4[T](elem1: T, elem2: T, elem3: T, elem4: T) {

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val network =
      (InputLayer having bias having 2.layers) +
      (ActivationSigmoid having bias having 3.layers) +
      (ActivationSigmoid having 1.layers)

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val procedure = input into network using (d => new ResilientPropagation(d.network, d.data)) until (_ < 0.01) giving ideal
val trainResult = procedure.get()

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val a = Array (
        Array(1,2,3),
        Array(4,5),
        Array(6)
    )
    
    //gives
    // 1 2 3
    // 4 5
    // 6

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def receive: PartialFunction[Any, Unit]

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Total {
    case Eat(food) ⇒
      println(s"Tasty $food!")
      Stopped
    case Wait() ⇒
      Same
}