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

  public MLDataPair get(int index) {
    if( index>=size() ) {
      return null;
    } 
    
    MLDataPair result = BasicMLDataPair.createPair(getInputSize(),
        this.getIdealSize());
    getRecord(index, result);
    return result;
  }

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    synchronized (this.data) {
      if (!hasNext()) {
        return null;
      }


      final MLDataPair pair = BasicMLDataPair.createPair(
          this.data.getInputSize(), this.data.getIdealSize());
      this.data.getRecord(this.current++, pair);
      return 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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  @Override
  public boolean read(final double[] input, final double[] ideal, final double[] significance) {
    if (!iterator.hasNext()) {
      return false;
    } else {
      MLDataPair pair = iterator.next();
      EngineArray.arrayCopy(pair.getInputArray(), input);
      EngineArray.arrayCopy(pair.getIdealArray(), ideal);
      significance[0] = pair.getSignificance();
      return true;
    }
  }

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  /**
   * {@inheritDoc}
   */
  @Override
  public void write(final double[] input, final double[] ideal, double significance) {
    MLDataPair pair = BasicMLDataPair.createPair(inputSize,
        idealSize);
    EngineArray.arrayCopy(input, pair.getIdealArray());
    EngineArray.arrayCopy(ideal, pair.getIdealArray());
    pair.setSignificance(significance);
  }

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    final Iterator<MLDataPair> seqIterator = sequence.iterator();
    seqIterator.next();


    for (int t = 0; t < (sequence.size() - 1); t++) {
      final MLDataPair observation = seqIterator.next();


      for (int i = 0; i < hmm.getStateCount(); i++) {
        for (int j = 0; j < hmm.getStateCount(); j++) {
          xi[t][i][j] = fbc.alphaElement(t, i)
              * hmm.getTransitionProbability(i, j)

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    for (final MLDataSet obsSeq : this.sequnces.getSequences()) {
      final ViterbiCalculator vc = new ViterbiCalculator(obsSeq, hmm);
      final int states[] = vc.stateSequence();


      for (int i = 0; i < states.length; i++) {
        final MLDataPair o = obsSeq.get(i);


        if (this.clusters.cluster(o) != states[i]) {
          modif = true;
          this.clusters.remove(o, this.clusters.cluster(o));
          this.clusters.put(o, states[i]);

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    final Iterator<MLDataPair> seqIterator = sequence.iterator();
    seqIterator.next();


    for (int t = 0; t < (sequence.size() - 1); t++) {
      final MLDataPair o = seqIterator.next();


      for (int i = 0; i < hmm.getStateCount(); i++) {
        for (int j = 0; j < hmm.getStateCount(); j++) {
          xi[t][i][j] = (fbc.alphaElement(t, i)
              * hmm.getTransitionProbability(i, j)

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      if (this.codec.getIdealSize() > 0) {
        b = new BasicMLData(ideal);
      }


      final MLDataPair pair = new BasicMLDataPair(a, b);
      pair.setSignificance(significance[0]);
      this.result.add(pair);


      currentRecord++;
      lastUpdate++;
      if (lastUpdate >= 10000) {

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    final long start = System.currentTimeMillis();
    final long stop = start + (10 * Evaluate.MILIS);
    int record = 0;


    MLDataPair pair = BasicMLDataPair.createPair(10, 10);


    int iterations = 0;
    while (System.currentTimeMillis() < stop) {
      iterations++;
      training.getRecord(record++, pair);

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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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