Examples of org.encog.ml.data.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

      network.reset();
      return network;
    }


    public static MLDataSet createNoisyXORDataSet(int count) {
      MLDataSet result = new BasicMLDataSet();
      for(int i=0;i<count;i++) {
        for(int j=0;j<4;j++) {
          MLData inputData = new BasicMLData(XOR_INPUT[j]);
          MLData idealData = new BasicMLData(XOR_IDEAL[j]);
          MLDataPair pair = new BasicMLDataPair(inputData,idealData);
          inputData.setData(0, inputData.getData(0)+RangeRandomizer.randomize(-0.1, 0.1));
          inputData.setData(1, inputData.getData(1)+RangeRandomizer.randomize(-0.1, 0.1));
          result.add(pair);
        }
      }
      return result;
    }

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public class TestLimited extends TestCase {
  
  public void testLimited()
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();    
    
    ResilientPropagation rprop = new ResilientPropagation(network,trainingData);
    rprop.iteration();
    rprop.iteration();

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      int input = dialog.getColumns().getValue();


      File targetFile = new File(EncogWorkBench.getInstance()
          .getProjectDirectory(), name);


      MLDataSet trainingData = RandomTrainingFactory.generate(
          System.currentTimeMillis(), elements, input, 0, low, high);


      EncogUtility.saveCSV(targetFile, CSVFormat.ENGLISH, trainingData);
    }
  }

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      ProjectEGFile popFile = dialog.getPopulation();
      NEATPopulation pop = (NEATPopulation) popFile.getObject();


      pop.setInputCount(2);
      pop.setOutputCount(1);
      MLDataSet training = dialog.getTrainingSet();


      if (dialog.getLoadToMemory().getValue()) {
        training = ((BufferedNeuralDataSet) training).loadToMemory();
      }

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    if (mlMethod != null)
      dialog.setMethod(mlMethod);


    if (dialog.process()) {
      MLMethod method = dialog.getNetwork();
      MLDataSet trainingData = dialog.getTrainingSet();


      if (method == null) {
        EncogWorkBench.displayError("Error",
            "Machine language method is required to train.");
        return;

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    network.addLayer(new BasicLayer(new ActivationSigmoidPosNeg(), true, 4));
    network.addLayer(new BasicLayer(new ActivationSigmoidPosNeg(), true, 1));
    network.getStructure().finalizeStructure();
    network.reset();


    final MLDataSet trainingSet = new BasicMLDataSet(
        CustomActivation.XOR_INPUT, CustomActivation.XOR_IDEAL);


    
    // train the neural network
    final MLTrain train = new ResilientPropagation(network, trainingSet);

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    dialog.getWeightTries().setValue(5);
    dialog.getIterations().setValue(25);
    dialog.getWindowSize().setValue(10);


    if (dialog.process()) {
      MLDataSet training = dialog.getTraining();      
      
      if( training == null ) {
        return null;
      }
      
      
      FeedForwardPattern pattern = new FeedForwardPattern();
      pattern.setInputNeurons(training.getInputSize());
      pattern.setOutputNeurons(training.getIdealSize());
    
      
      pattern.setActivationFunction(dialog.getActivationFunction());      
      IncrementalPruneTab tab = new IncrementalPruneTab(
          dialog.getIterations().getValue(),

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public class TestFolded extends TestCase {
  @Test
  public void testRPROP() throws Throwable
  {
    MLDataSet trainingData = XOR.createNoisyXORDataSet(10);
    
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    
    final FoldedDataSet folded = new FoldedDataSet(trainingData); 
    final MLTrain train = new ResilientPropagation(network, folded);

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 */
public class XORCSV {


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


    final MLDataSet trainingSet = TrainingSetUtil.loadCSVTOMemory(
        CSVFormat.ENGLISH, "c:\\temp\\xor.csv", false, 2, 1);
    final BasicNetwork network = EncogUtility.simpleFeedForward(2, 4, 0, 1,
        true);


    System.out.println();

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  @Test
  public void testSOM() {


    // create the training set
    final MLDataSet training = new BasicMLDataSet(
        TestCompetitive.SOM_INPUT, null);


    // Create the neural network.
    SOM network = new SOM(4,2);    
    network.setWeights(new Matrix(MATRIX_ARRAY));

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