mgo.evolution.algorithm.HyperNEAT.scala Maven / Gradle / Ivy

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/*
 * Copyright (C) 08/07/2015 Guillaume Chérel
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see .
 */

package mgo.evolution.algorithm

//import mgo.evolution._
//import mgo.evolution.breed.NEATBreedingContext
//import mgo.evolution.crossover.NEATCrossover
//import mgo.evolution.mutation.NEATMutation
//import mgo.evolution.problem.NEATProblem
//import mgo.selection.NEATMating
//import mgo.tools.neuralnetwork.ActivationFunction
//
//import mgo.evolution.genome.NEATGenome._
//
//import util.Random

/**
 * Differences with NEAT:
 * - Nodes carry activation functions
 * - Neural nets are created from the evolved nets
 */
/*trait HyperNEAT <: NEATProblem with GeneticBreeding with NEATBreedingContext with NEATMating with NEATCrossover with NEATMutation with NEATElitism with NEATArchive with NoPhenotype with Cloning {

  type ACTIVF = String
  val cppnActivationFunctions: Seq[ACTIVF]

  type NODEDATA = ACTIVF

  def pickActivationFunction(implicit rng: Random): ACTIVF = cppnActivationFunctions(rng.nextInt(cppnActivationFunctions.length))

  def pickNewHiddenNode(level: Double)(implicit rng: Random): HiddenNode =
    HiddenNode(
      pickActivationFunction,
      level)

  def newInputNode: InputNode = InputNode("lin")
  def newBiasNode: BiasNode = BiasNode("lin")
  def newOutputNode: OutputNode = OutputNode("lin")
}*/