Package org.encog.mathutil.randomize

Examples of org.encog.mathutil.randomize.ConsistentRandomizer

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  /**
   * {@inheritDoc}
   */
  @Override
  public final void reset(final int seed) {
    final ConsistentRandomizer randomize = new ConsistentRandomizer(-1, 1,
        seed);
    randomize.randomize(this.weightsInputToInstar);
    randomize.randomize(this.weightsInstarToOutstar);
  }
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      network.addLayer(new BasicLayer(null, false, 2));
      network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
      network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 1));
      network.getStructure().finalizeStructure();
      network.reset();
      (new ConsistentRandomizer(0,0.5,i)).randomize(network);

      // create training data
      MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);

      // train the neural network
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  }

  public static FlatNetwork createNetwork() {
    BasicNetwork network = EncogUtility
        .simpleFeedForward(2, 4, 0, 1, false);
    Randomizer randomizer = new ConsistentRandomizer(-1, 1);
    randomizer.randomize(network);
    return network.getStructure().getFlat().clone();
  }
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  public void testCompleteTrain()
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    BasicNetwork network = EncogUtility.simpleFeedForward(2, 5, 7, 1, true);
    Randomizer randomizer = new ConsistentRandomizer(-1, 1, 19);
    //randomizer.randomize(network);
    System.out.println(network.dumpWeights());
    MLTrain rprop = new ResilientPropagation(network, trainingData);
    int iteration = 0;
    do {
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    network.addLayer(new BasicLayer(null,true,2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,4));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
    network.getStructure().finalizeStructure();
   
    (new ConsistentRandomizer(-1,1)).randomize(network);
   
    return network;
  }
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  private void optionConsistent(RandomizeNetworkDialog dialog) {
    int seed = dialog.getSeedValue().getValue();
    double min = dialog.getConstLow().getValue();
    double max = dialog.getConstHigh().getValue();
    ConsistentRandomizer c = new ConsistentRandomizer(min, max, seed);
    c.randomize(this.method);
    setDirty(true);
  }
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    network.addLayer(new BasicLayer(null,false,2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,1));
    network.getStructure().finalizeStructure();
    network.reset();
    new ConsistentRandomizer(-1,1).randomize(network);

    // create training data
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    final MLTrain train = new ResilientPropagation(network, trainingSet);
    //
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  /**
   * {@inheritDoc}
   */
  @Override
  public void reset(final int seed) {
    final ConsistentRandomizer randomizer = new ConsistentRandomizer(-1, 1,
        seed);

    /**
     * {@inheritDoc}
     */
    performConnectionTask(new ConnectionTask() {
      @Override
      public void task(final FreeformConnection connection) {
        connection.setWeight(randomizer.nextDouble());
      }
    });
  }
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  /**
   * {@inheritDoc}
   */
  @Override
  public void reset(final int seed) {
    final ConsistentRandomizer randomize = new ConsistentRandomizer(-1, 1,
        seed);
    randomize.randomize(this.weightsInputToInstar);
    randomize.randomize(this.weightsInstarToOutstar);
  }
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   * RangeRandomizer with a range between -1 and 1.
   *
   */
  @Override
  public void reset(final int seed) {
    (new ConsistentRandomizer(-1,1,seed)).randomize(this);
  }
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Related Classes of org.encog.mathutil.randomize.ConsistentRandomizer

  • org.encog.examples.neural.benchmark.TestConverge
  • org.encog.examples.neural.xor.XORDisplay
  • org.encog.mathutil.LinearCongruentialGenerator
  • org.encog.mathutil.matrixes.hessian.TestHessian
  • org.encog.mathutil.randomize.generate.LinearCongruentialRandom
  • org.encog.neural.cpn.CPN
  • org.encog.neural.freeform.FreeformNetwork
  • org.encog.neural.networks.BasicNetwork
  • org.encog.neural.networks.NetworkUtil
  • org.encog.neural.networks.TestConsistency
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