Package org.encog.ml.data

Examples of org.encog.ml.data.MLDataSet

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   */
  @Override
  public final boolean executeCommand(final String args) {

    this.kfold = obtainCross();
    final MLDataSet trainingSet = obtainTrainingSet();
    MLMethod method = obtainMethod();
    final MLTrain trainer = createTrainer(method, trainingSet);
   
    EncogLogging.log(EncogLogging.LEVEL_DEBUG, "Beginning training");

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

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

    MLDataSet trainingSet = EncogUtility.loadEGB2Memory(trainingFile);

    if (this.kfold > 0) {
      trainingSet = new FoldedDataSet(trainingSet);
    }
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    network.getStructure().finalizeStructure();
    network.reset();
    new ConsistentRandomizer(-1,1).randomize(network);

    // create training data
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    final MLTrain train = new ResilientPropagation(network, trainingSet);
    //
    int epoch = 1;
    do {
      train.iteration();
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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);
    pop.setInitialConnectionDensity(1.0);// not required, but speeds training
    pop.reset();

    CalculateScore score = new TrainingSetScore(trainingSet);
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  public void run()
  {
    normalizeSunspots(0.1,0.9);
    SVM network = createNetwork();
    MLDataSet training = generateTraining();
    train(network,training);
    predict(network);
   
  }
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        }else{
            neuralNetwork=(BasicNetwork)EncogDirectoryPersistence.loadObject(file);
        }
    }
    private Object getNeuralNetworkTrainingData(NeuralData nd){
        MLDataSet trainingSet=new BasicMLDataSet();
       
        MLData mdInput=new BasicMLData(nd.getInputVector());
        MLData mdOuput=new BasicMLData(nd.getOutputVector());
       
               
        trainingSet.add(mdInput, mdOuput);
       
        return trainingSet;
    }
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        return trainingSet;
    }
   
    private Object getNeuralNetworkTrainingData(NeuralData [] ndArray){
        MLDataSet trainingSet=new BasicMLDataSet();
       
        for(int i=0;i<ndArray.length;i++){
            MLData mdInput=new BasicMLData(ndArray[i].getInputVector());
            MLData mdOuput=new BasicMLData(ndArray[i].getOutputVector());
   
            trainingSet.add(mdInput, mdOuput);
        }
        return trainingSet;
    }
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  }

  private void embedTraining(final EncogProgramNode node) {

    final File dataFile = (File) node.getArgs().get(0).getValue();
    final MLDataSet data = EncogUtility.loadEGB2Memory(dataFile);
    addInclude("Encog.ML.Data.Basic");
   
    // generate the input data

    indentLine("public static readonly double[][] INPUT_DATA = {");
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  }

  private void embedTraining(final EncogProgramNode node) {

    final File dataFile = (File) node.getArgs().get(0).getValue();
    final MLDataSet data = EncogUtility.loadEGB2Memory(dataFile);

    // generate the input data

    indentLine("var INPUT_DATA = [");
    for (final MLDataPair pair : data) {
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  }

  private void embedTraining(final EncogProgramNode node) {

    final File dataFile = (File) node.getArgs().get(0).getValue();
    final MLDataSet data = EncogUtility.loadEGB2Memory(dataFile);

    // generate the input data

    indentLine("public static final double[][] INPUT_DATA = {");
    for (final MLDataPair pair : data) {
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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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