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

  @Override
  public void performJobUnit(final JobUnitContext context) {


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


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

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    final String trainingID = prop.getPropertyString(
        ScriptProperties.ML_CONFIG_TRAINING_FILE);


    final File trainingFile = this.encogAnalyst.getScript().resolveFilename(trainingID);


    MLDataSet trainingSet = EncogUtility.loadEGB2Memory(trainingFile);
    return trainingSet;
  }

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    Assert.assertTrue(e<targetError);
  }
  
  public double calculateError() {
    MLMethod method = obtainMethod();
    MLDataSet data = obtainTrainingSet();
    return ((MLError)method).calculateError(data);
  }

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     */
    public static MLDataSet loadCSV2Memory(String filename, int input, int ideal, boolean headers, CSVFormat format, boolean significance)
    {
        DataSetCODEC codec = new CSVDataCODEC(new File(filename), format, headers, input, ideal, significance);
        MemoryDataLoader load = new MemoryDataLoader(codec);
        MLDataSet dataset = load.external2Memory();
        return dataset;
    }

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  }


  public static MLDataSet loadEGB2Memory(File filename) {
    BufferedMLDataSet buffer = new BufferedMLDataSet(filename);
    MLDataSet result = buffer.loadToMemory();
    buffer.close();
    return result;
  }

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    int dss = dataSetFactory.getInputData().size();
    for (int k = 0; k < dss; k++)
      D.add(1.0 / (float) dss);
    for (int i = 0; i < T; i++) {
      dataSetFactory.setSignificance(D);
      MLDataSet thisSet = dataSetFactory.getNewDataSet();
      GenericEnsembleML newML = new GenericEnsembleML(mlFactory.createML(dataSetFactory.getInputData().getInputSize(), dataSetFactory.getInputData().getIdealSize()),mlFactory.getLabel());
      do {
        mlFactory.reInit(newML.getMl());
        MLTrain train = trainFactory.getTraining(newML.getMl(), thisSet);
        newML.setTraining(train);

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  public final File EG_FILENAME = TEMP_DIR.createFile("encogtest.eg");
  public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
  
  private NEATPopulation generate()
  {
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);
    
    CalculateScore score = new TrainingSetScore(trainingSet);
    // train the neural network
    ActivationStep step = new ActivationStep();
    step.setCenter(0.5);

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  {
    Assert.assertEquals(10,pop.getPopulationSize());
    Assert.assertEquals(0.2,pop.getSurvivalRate());
    
    // see if the population can actually be used to train
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);    
    CalculateScore score = new TrainingSetScore(trainingSet);
    EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop, score);
    train.iteration();


  }

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  public final TempDir TEMP_DIR = new TempDir();
  public final File EG_FILENAME = TEMP_DIR.createFile("encogtest.eg");
  public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");


  public void testRPROPCont() {
    MLDataSet trainingSet = XOR.createXORDataSet();
    BasicNetwork net1 = XOR.createUnTrainedXOR();
    BasicNetwork net2 = XOR.createUnTrainedXOR();
    
    ResilientPropagation rprop1 = new ResilientPropagation(net1,trainingSet);
    ResilientPropagation rprop2 = new ResilientPropagation(net2,trainingSet);

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      Assert.assertEquals(net1.getFlat().getWeights()[i], net2.getFlat().getWeights()[i],0.0001);
    }
  }
  
  public void testRPROPContPersistEG() {
    MLDataSet trainingSet = XOR.createXORDataSet();
    BasicNetwork net1 = XOR.createUnTrainedXOR();
    BasicNetwork net2 = XOR.createUnTrainedXOR();
    
    ResilientPropagation rprop1 = new ResilientPropagation(net1,trainingSet);
    ResilientPropagation rprop2 = new ResilientPropagation(net2,trainingSet);

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