Examples of org.encog.ml.data.MLDataPair

org.encog.ml.data.MLDataPair
Training data is stored in two ways, depending on if the data is for supervised, or unsupervised training. For unsupervised training just an input value is provided, and the ideal output values are null. For supervised training both input and the expected ideal outputs are provided. This interface abstracts classes that provide a holder for both of these two data items. @author jheaton

      if (!iterator.hasNext()) {
        return false;
      }
      final MLDataFieldHolder holder = this.dataSetIteratorMap
          .get(iterator);
      final MLDataPair pair = iterator.next();
      holder.setPair(pair);
    }


    // see if any of the arrays want to stop
    for (final InputField field : this.inputFields) {

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   *            The size of the ideal data.
   * @return A new data pair object.
   */
  public static MLDataPair createPair(final int inputSize, 
      final int idealSize) {
    MLDataPair result;


    if (idealSize > 0) {
      result = new BasicMLDataPair(new BasicMLData(inputSize),
          new BasicMLData(idealSize));
    } else {

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   * {@inheritDoc}
   */
  @Override
  public void add(final MLData inputData, final MLData idealData) {


    final MLDataPair pair = new BasicMLDataPair(inputData, idealData);
    this.currentSequence.add(pair);
  }

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    return (int)getRecordCount();
  }


  @Override
  public MLDataPair get(int index) {
    MLDataPair result = BasicMLDataPair.createPair(getInputSize(), getIdealSize());
    this.getRecord(index, result);
    return result;
  }

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        return null;
      }
      
      MLDataSet target = sequences.get(this.currentSequenceIndex);
      
      MLDataPair result = ((BasicMLDataSet)target).getData().get(this.currentIndex);
      this.currentIndex++;
      if( this.currentIndex>=target.getRecordCount()) {
        this.currentIndex = 0;
        this.currentSequenceIndex++;
      }

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      // handle predict window
      for (int j = 0; j < this.predictWindow; j++) {
        idealData.setData(j, data[index++]);
      }


      final MLDataPair pair = new BasicMLDataPair(inputData,
          idealData);
      result.add(pair);
    }


    return result;

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     * @return MLDatapair containing data.
     */
    public MLDataPair processToPair(double[] data)
    {


        MLDataPair pair = null;
        int totalWindowSize = inputWindow + predictWindow;
        int stopPoint = data.length - totalWindowSize;


        for (int i = 0; i < stopPoint; i++)
        {

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    BasicMLData input = new BasicMLData(
        MatrixMLDataSet.this.calculatedInputSize * calculateLagCount());
    BasicMLData ideal = new BasicMLData(
        MatrixMLDataSet.this.calculatedIdealSize * calculateLeadCount());
    MLDataPair pair = new BasicMLDataPair(input, ideal);


    MatrixMLDataSet.this.getRecord(index, pair);


    return pair;
  }

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   * {@inheritDoc}
   */
  @Override
  public void add(final MLData inputData, final MLData idealData) {


    final MLDataPair pair = new BasicMLDataPair(inputData, idealData);
    this.data.add(pair);
  }

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  @Override
  public int getIdealSize() {
    if (this.data.isEmpty()) {
      return 0;
    }
    final MLDataPair first = this.data.get(0);
    if (first.getIdeal() == null) {
      return 0;
    }


    return first.getIdeal().size();
  }

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org.encog.app.analyst.util.AnalystUtility

org.encog.ensemble.adaboost.AdaBoost

org.encog.ensemble.data.EnsembleDataSet

org.encog.examples.clustering.kmeans.SimpleKMeans

org.encog.examples.neural.benchmark.BinaryVsMemory

org.encog.ml.data.auto.AutoFloatDataSet

org.encog.ml.data.auto.TestAutoFloatDataSet

org.encog.ml.data.basic.BasicMLDataPair

org.encog.ml.data.basic.BasicMLDataSet

org.encog.ml.data.basic.BasicMLSequenceSet

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