Examples of BasicNetwork

org.encog.neural.networks.BasicNetwork
This class implements a neural network. This class works in conjunction the Layer classes. Layers are added to the BasicNetwork to specify the structure of the neural network. The first layer added is the input layer, the final layer added is the output layer. Any layers added between these two layers are the hidden layers. The network structure is stored in the structure member. It is important to call: network.getStructure().finalizeStructure(); Once the neural network has been completely constructed.

Examples of org.encog.neural.networks.BasicNetwork

    }
  }


  public void performClose() {
    if (this.prune != null) {
      BasicNetwork network = this.prune.getBestNetwork();


      if (network != null) {
        if (EncogWorkBench.askQuestion("Network",
            "Do you wish to save this network?")) {
          if (network != null) {

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Examples of org.encog.neural.networks.BasicNetwork

      this.activationOutput = this.activationHidden;
    
    final Layer input = new BasicLayer(null, true,
        this.inputNeurons);


    final BasicNetwork result = new BasicNetwork();
    result.addLayer(input);




    for (final Integer count : this.hidden) {


      final Layer hidden = new BasicLayer(this.activationHidden, true, count);


      result.addLayer(hidden);
    }


    final Layer output = new BasicLayer(this.activationOutput, false,
        this.outputNeurons);
    result.addLayer(output);


    result.getStructure().finalizeStructure();
    result.reset();


    return result;
  }

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Examples of org.encog.neural.networks.BasicNetwork

   *            Contains information about the job unit.
   */
  @Override
  public final void performJobUnit(final JobUnitContext context) {


    final BasicNetwork network = (BasicNetwork) context.getJobUnit();
    BufferedNeuralDataSet buffer = null;
    MLDataSet useTraining = this.training;


    if (this.training instanceof BufferedNeuralDataSet) {
      buffer = (BufferedNeuralDataSet) this.training;
      useTraining = buffer.openAdditional();
    }


    // train the neural network


    double error = Double.POSITIVE_INFINITY;
    for (int z = 0; z < this.weightTries; z++) {
      network.reset();
      final Propagation train = new ResilientPropagation(network,
          useTraining);
      final StopTrainingStrategy strat = new StopTrainingStrategy(0.001,
          5);


      train.addStrategy(strat);
      train.setNumThreads(1); // force single thread mode


      for (int i = 0; (i < this.iterations) && !getShouldStop()
          && !strat.shouldStop(); i++) {
        train.iteration();
      }


      error = Math.min(error, train.getError());
    }


    if (buffer != null) {
      buffer.close();
    }


    if (!getShouldStop()) {
      // update min and max


      this.high = Math.max(this.high, error);
      this.low = Math.min(this.low, error);


      if (this.hidden1Size > 0) {
        int networkHidden1Count;
        int networkHidden2Count;


        if (network.getLayerCount() > 3) {
          networkHidden2Count = network.getLayerNeuronCount(1);
          networkHidden1Count = network.getLayerNeuronCount(2);
        } else {
          networkHidden2Count = 0;
          networkHidden1Count = network.getLayerNeuronCount(1);
        }


        int row, col;


        if (this.hidden2Size == 0) {

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Examples of org.encog.neural.networks.BasicNetwork

  public final Object requestNextTask() {
    if (this.done || getShouldStop()) {
      return null;
    }


    final BasicNetwork network = generateNetwork();


    if (!increaseHiddenCounts()) {
      this.done = true;
    }

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Examples of org.encog.neural.networks.BasicNetwork

    }


    // now select the best network, which is the most simple of the
    // top networks.


    BasicNetwork choice = null;


    for (final BasicNetwork n : this.topNetworks) {
      if (n == null) {
        continue;
      }


      if (choice == null) {
        choice = n;
      } else {
        if (n.getStructure().calculateSize() < choice.getStructure()
            .calculateSize()) {
          choice = n;
        }
      }
    }

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Examples of org.encog.neural.networks.BasicNetwork

   * Generate the network.
   * 
   * @return The generated network.
   */
  public final MLMethod generate() {
    final BasicNetwork network = new BasicNetwork();


    final Layer inputLayer = new BasicLayer(new ActivationLinear(), true,
        this.inputNeurons);
    final Layer outputLayer = new BasicLayer(new ActivationLinear(), false,
        this.outputNeurons);


    network.addLayer(inputLayer);
    network.addLayer(outputLayer);
    network.getStructure().finalizeStructure();


    (new RangeRandomizer(-0.5, 0.5)).randomize(network);


    return network;
  }

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Examples of org.encog.neural.networks.BasicNetwork

    getGenetic().setCrossover(
        new Splice(network.getStructure().calculateSize() / 3));
    getGenetic().setMutate(new MutatePerturb(4.0));
    getGenetic().setPopulation(population);
    for (int i = 0; i < population.getPopulationSize(); i++) {
      final BasicNetwork chromosomeNetwork = (BasicNetwork) network
          .clone();
      randomizer.randomize(chromosomeNetwork);


      final NeuralGenome genome = new NeuralGenome(chromosomeNetwork);
      genome.setGeneticAlgorithm(getGenetic());

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Examples of org.encog.neural.networks.BasicNetwork


    int failureCount = 0;
    
    for(int i=0;i<1000;i++) {
      // create a neural network, without using a factory
      BasicNetwork network = new BasicNetwork();
      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);

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Examples of org.encog.neural.networks.BasicNetwork

    return temp.process(this.normalizedSunspots);
  }
  
  public BasicNetwork createNetwork()
  {
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(WINDOW_SIZE));
    network.addLayer(new BasicLayer(10));
    network.addLayer(new BasicLayer(1));
    network.getStructure().finalizeStructure();
    network.reset();
    return network;
  }

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Examples of org.encog.neural.networks.BasicNetwork

  }
  
  public void run()
  {
    normalizeSunspots(0.1,0.9);
    BasicNetwork network = createNetwork();
    MLDataSet training = generateTraining();
    train(network,training);
    predict(network);
    
  }

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