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/*
 * Copyright (C) 08/01/13 Guillaume Chérel, Romain Reuillon
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Affero 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 Affero 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.test

import mgo.evolution._

object SphereProfile extends App {

  import algorithm._

  //Profile the first dimension of the genome
  val algo: Profile[Int] = Profile(
    lambda = 100,
    fitness = discreteSphere.compute,
    niche = Profile.continuousProfile(x = 0, nX = 10),
    continuous = discreteSphere.continuous(2),
    discrete = discreteSphere.discrete(2))

  def evolution: RunAlgorithm[Profile[Int], CDGenome.DeterministicIndividual.Individual[Vector[Double]], CDGenome.Genome, Profile.ProfileState] =
    algo.
      until(afterGeneration(1000)).
      trace((s, is) => println(s.generation))

  val (finalState, finalPopulation) = evolution.eval(new util.Random(42))

  println(
    Profile.result(algo, finalPopulation).map {
      r => (r.continuous ++ r.discrete ++ r.fitness).mkString(",")
    }.mkString("\n"))

}

object NoisySphereProfile extends App {

  import algorithm._

  def aggregation(history: Vector[Vector[Double]]): Vector[Double] = history.transpose.map(h => h.sum / h.size)

  val algo: NoisyProfile[Int, Vector[Double]] = NoisyProfile(
    muByNiche = 20,
    lambda = 100,
    fitness = noisyDiscreteSphere.compute,
    aggregation = aggregation,
    niche = NoisyProfile.continuousProfile[Vector[Double]](x = 0, nX = 10),
    continuous = noisyDiscreteSphere.continuous(2),
    discrete = noisyDiscreteSphere.discrete(2))

  def evolution: RunAlgorithm[NoisyProfile[Int, Vector[Double]], CDGenome.NoisyIndividual.Individual[Vector[Double]], CDGenome.Genome, NoisyProfile.ProfileState] =
    algo.
      until(afterGeneration(1000)).
      trace((s, is) => println(s.generation))

  val (finalState, finalPopulation) = evolution.eval(new util.Random(42))

  println(
    NoisyProfile.result(algo, finalPopulation).map {
      r => (r.continuous ++ r.discrete ++ r.fitness ++ Vector(r.replications)).mkString(",")
    }.mkString("\n"))

}