Package eas.users.students.benediktMueller.qswarm.neural

Examples of eas.users.students.benediktMueller.qswarm.neural.SparseNetBenedikt


    this.robots = robots;
    this.stigmergy = stigmergy;
    this.rand = rand;
    fitness = 0.0;
   
    net = new SparseNetBenedikt(2*robots[0].sensors.length, 2, 4); // two inputs per sensor ray (distance and dirt)
   
    for(int i = 0; i < 100; i++)
    {
      net.mutate(rand);
    }
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  {
    SparseNetBenedikt.recurrent = false;
   
    rand = new Random(System.currentTimeMillis());
    //net = new SparseNet(2, 3, 7);
    net = new SparseNetBenedikt();
    net2 = new SparseNetBenedikt();
//    img = new BufferedImage(w, h, BufferedImage.TYPE_INT_ARGB);
    img = new BufferedImage(2*w, h, BufferedImage.TYPE_INT_ARGB);
    //for(int i = 0; i < 10000; i++) net.mutate(rand);
   
    createImg();
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    @Override
  public void init()
  {
    rand = new Random(System.currentTimeMillis());
    //net = new SparseNet(2, 3, 7);
    net = new SparseNetBenedikt();
    img = new BufferedImage(w, h, BufferedImage.TYPE_INT_ARGB);
    //for(int i = 0; i < 10000; i++) net.mutate(rand);
   
    createImg();
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  public Approximator(TrainingSet set)
  {
    lock = new ReentrantLock();
    this.set = set;
    net = new SparseNetBenedikt();
    //for(int i = 0; i < 1000; i++) net.mutate(rand);
  }
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    @Override
  public void init()
  {
    rand = new Random(System.currentTimeMillis());
    net = new SparseNetBenedikt(2, 3, 50);
    img = new BufferedImage(w, h, BufferedImage.TYPE_INT_ARGB);
    for(int i = 0; i < 10000; i++) net.mutate(rand);
   
    createImg();
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