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

    try {
      EvaluateDialog dialog = new EvaluateDialog(EncogWorkBench
          .getInstance().getMainWindow());
      if (dialog.process()) {
        MLMethod method = dialog.getNetwork();
        MLDataSet training = dialog.getTrainingSet();


        double error = 0;


        if (method instanceof MLError) {
          error = ((MLError) method).calculateError(training);

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Examples of org.encog.ml.data.MLDataSet

  @Override
  public final void performJobUnit(final JobUnitContext context) {


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


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

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Examples of org.encog.ml.data.MLDataSet

      network.getStructure().finalizeStructure();
      network.reset();
      (new ConsistentRandomizer(0,0.5,i)).randomize(network);


      // create training data
      MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);


      // train the neural network
      final ResilientPropagation train = new ResilientPropagation(network, trainingSet);
      train.setRPROPType(RPROPType.iRPROPp);

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Examples of org.encog.ml.data.MLDataSet

  
  public void run()
  {
    normalizeSunspots(0.1,0.9);
    BasicNetwork network = createNetwork();
    MLDataSet training = generateTraining();
    train(network,training);
    predict(network);
    
  }

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Examples of org.encog.ml.data.MLDataSet

  
  public void run()
  {
    normalizeSunspots(0.1,0.9);
    SVM network = createNetwork();
    MLDataSet training = generateTraining();
    train(network,training);
    predict(network);
    
  }

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Examples of org.encog.ml.data.MLDataSet

     */
    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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Examples of org.encog.ml.data.MLDataSet

   * Create a dataset from the clustered data.
   * @return The dataset.
   */
  @Override
  public final MLDataSet createDataSet() {
    final MLDataSet result = new BasicMLDataSet();


    for (final MLData dataItem : this.data) {
      result.add(dataItem);
    }


    return result;
  }

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Examples of org.encog.ml.data.MLDataSet


    // Display the cluster
    int i = 1;
    for (final MLCluster cluster : kmeans.getClusters()) {
      System.out.println("*** Cluster " + (i++) + " ***");
      final MLDataSet ds = cluster.createDataSet();
      final MLDataPair pair = BasicMLDataPair.createPair(
          ds.getInputSize(), ds.getIdealSize());
      for (int j = 0; j < ds.getRecordCount(); j++) {
        ds.getRecord(j, pair);
        System.out.println(Arrays.toString(pair.getInputArray()));


      }
    }
  }

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Examples of org.encog.ml.data.MLDataSet

    network.addLayer(new BasicLayer(2));
    network.addLayer(new BasicLayer(1));
    network.getStructure().finalizeStructure();
    network.reset();


    MLDataSet trainingSet = new SQLNeuralDataSet(
        XORSQL.SQL,
        XORSQL.INPUT_SIZE,
        XORSQL.IDEAL_SIZE,
        XORSQL.SQL_DRIVER,
        XORSQL.SQL_URL,

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Examples of org.encog.ml.data.MLDataSet

  }
  
  public static void evaluate() {
    int[] count = new int[TRIES];
    
    MLDataSet trainingData = generateTraining(INPUT_OUTPUT_COUNT, COMPL);
    
    for(int i=0;i<TRIES;i++) {
    
      MLMethod method = EncogUtility.simpleFeedForward(INPUT_OUTPUT_COUNT,
          HIDDEN_COUNT, 0, INPUT_OUTPUT_COUNT, false);

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