Examples of MLDataSet

org.encog.ml.data.MLDataSet
An interface designed to abstract classes that store machine learning data. This interface is designed to provide EngineDataSet objects. These can be used to train machine learning methods using both supervised and unsupervised training. Some implementations of this interface are memory based. That is they store the entire contents of the dataset in memory. Other implementations of this interface are not memory based. These implementations read in data as it is needed. This allows very large datasets to be used. Typically the add methods are not supported on non-memory based datasets. @author jheaton

Examples of org.encog.ml.data.MLDataSet

  
  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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Examples of org.encog.ml.data.MLDataSet

    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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Examples of org.encog.ml.data.MLDataSet

  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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Examples of org.encog.ml.data.MLDataSet

    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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Examples of org.encog.ml.data.MLDataSet

    { 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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Examples of org.encog.ml.data.MLDataSet

    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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Examples of org.encog.ml.data.MLDataSet

    } while(train.getError()>0.01); 
  }
  
  public MLDataSet generateTraining(double[][] input,double[][] ideal)
  {
    MLDataSet result = new BasicMLDataSet(input,ideal);
    return result;
  }

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Examples of org.encog.ml.data.MLDataSet

  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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Examples of org.encog.ml.data.MLDataSet


  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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Examples of org.encog.ml.data.MLDataSet

    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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