lalala.scala

//
trait Model[T] {
  def name: String // TODO: not sure of its utility
  def randomPick: Seq[Double]
}

trait ContinuousModel[T] extends Model[T] {
  implicit val continuousOpt: Continuous[T]
}

trait GaussianModel[T] extends ContinuousModel[T] {
  def name: String
  def mean: Seq[Double]
  def stdDev: Seq[Double]
  def randomPick: Seq[Double] = {
    // TODO: using mean and variance select a point on that Gaussian. Then peg it to the constraints to have an actual point
    Seq.empty // FIXME
  }
}

// 

trait Optimizable[T] {
  // TODO: express the fact that "0" is better
  def optimize(m: Model[T], fitnessF: T => Set[(Double, Probability)]): Option[T]
}

object Optimizable {
  trait OptimizableGaussian[T] extends Optimizable[T] {
    implicit val ops: Continuous[T]
    def optimize(m: GaussianModel[T], fitnessF: T => Set[(Double, Probability)]): Option[T] = {
      ops.fromDouble(m.mean) // TODO: whatever! I just wanted to express that the optimization could differ by family of Models.
    }
  }

}