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")
}*/
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