Package org.encog.ml.data

Examples of org.encog.ml.data.MLDataSet

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  public static int evaluateTrain(int input, int hidden1, int hidden2,
      int output) {
    final BasicNetwork network = EncogUtility.simpleFeedForward(input,
        hidden1, hidden2, output, true);
    final MLDataSet training = RandomTrainingFactory.generate(1000,
        10000, input, output, -1, 1);
 
   
    return evaluateTrain(network, training);
  }
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    return network.getStructure().getFlat().clone();
  }

  public static void main(String[] args) {
   
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);

    FlatNetwork network = createNetwork();

    System.out.println("Starting Weights:");
    displayWeights(network);
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  public static void main(final String args[]) {
   
    FlatNetwork network = new FlatNetwork(2,4,0,1,false);
    network.randomize();
   
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
   
   
    TrainFlatNetworkResilient train = new TrainFlatNetworkResilient(network,trainingSet);
   
    //Encog.getInstance().initCL();
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    network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
    network.getStructure().finalizeStructure();
    network.reset();

    // create training data
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
   
    // train the neural network
    final ResilientPropagation train = new ResilientPropagation(network, trainingSet);

    int epoch = 1;
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    { 1.0, 1.0, -1.0, -1.0 } };
 
  public static void main(String args[])
  { 
    // create the training set
    MLDataSet training = new BasicMLDataSet(SOM_INPUT,null);
   
    // Create the neural network.
    SOM network = new SOM(4,2);
    network.reset();
   
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    network.addLayer(new BasicLayer(MultiBench.HIDDEN_COUNT));
    network.addLayer(new BasicLayer(MultiBench.OUTPUT_COUNT));
    network.getStructure().finalizeStructure();
    network.reset();
   
    final MLDataSet training = RandomTrainingFactory.generate(1000,50000,
        INPUT_COUNT, OUTPUT_COUNT, -1, 1);
   
    ResilientPropagation rprop = new ResilientPropagation(network,training);
    rprop.setNumThreads(thread);
    for(int i=0;i<5;i++)
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    } while(train.getError()>0.01);
  }
 
  public MLDataSet generateTraining(double[][] input,double[][] ideal)
  {
    MLDataSet result = new BasicMLDataSet(input,ideal);
    return result;
  }
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  public void run()
  {
    prepareInput();
    normalizeInput();
    CPN network = createNetwork();
    MLDataSet training = generateTraining(this.input1,this.ideal1);
    trainInstar(network,training);
    trainOutstar(network,training);
    test(network,PATTERN1,this.input1);
  }
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  public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };

  public static void main(final String args[]) {

    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    NEATPopulation pop = new NEATPopulation(2,1,1000);
    CalculateScore score = new TrainingSetScore(trainingSet);
    // train the neural network
    ActivationStep step = new ActivationStep();
    step.setCenter(0.5);
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    try {
      final int inputNeuron = OCR.DOWNSAMPLE_HEIGHT
          * OCR.DOWNSAMPLE_WIDTH;
      final int outputNeuron = this.letterListModel.size();

      final MLDataSet trainingSet = new BasicMLDataSet();
      for (int t = 0; t < this.letterListModel.size(); t++) {
        final MLData item = new BasicMLData(inputNeuron);
        int idx = 0;
        final SampleData ds = (SampleData) this.letterListModel
            .getElementAt(t);
        for (int y = 0; y < ds.getHeight(); y++) {
          for (int x = 0; x < ds.getWidth(); x++) {
            item.setData(idx++, ds.getData(x, y) ? .5 : -.5);
          }
        }

        trainingSet.add(new BasicMLDataPair(item, null));
      }

      this.net = new SOM(inputNeuron,outputNeuron);
      this.net.reset();

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Related Classes of org.encog.ml.data.MLDataSet

  • com.wiieditor.neuralnetwork.NeuralNetworkWrapper
  • org.encog.app.analyst.AnalystTestingUtility
  • org.encog.app.analyst.commands.Cmd
  • org.encog.app.analyst.commands.CmdTrain
  • org.encog.app.analyst.util.AnalystUtility
  • org.encog.app.generate.generators.cs.GenerateCS
  • org.encog.app.generate.generators.java.GenerateEncogJava
  • org.encog.app.generate.generators.js.GenerateEncogJavaScript
  • org.encog.ensemble.adaboost.AdaBoost
  • org.encog.examples.clustering.kmeans.SimpleKMeans
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