Examples of org.encog.engine.data.EngineData

org.encog.engine.data.EngineData
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

   * implementation of EngineIndexableSet interface. It is added for
   * Encog-Engine compatibility.
   */
  @Override
  public void getRecord(long index, EngineData pair) {
    EngineData item = this.elements.get((int) index);
    pair.setInputArray(item.getInputArray());
    pair.setIdealArray(item.getIdealArray());
  }

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   */
  public double calculateError(final EngineIndexableSet data) {
    final ErrorCalculation errorCalculation = new ErrorCalculation();


    final double[] actual = new double[this.outputCount];
    final EngineData pair = BasicEngineData.createPair(data.getInputSize(),
        data.getIdealSize());


    for (int i = 0; i < data.getRecordCount(); i++) {
      data.getRecord(i, pair);
      compute(pair.getInputArray(), actual);
      errorCalculation.updateError(actual, pair.getIdealArray());
    }
    return errorCalculation.calculate();
  }

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  public void setTraining(EngineIndexableSet training) {
    this.training = training;
    this.trainingLength = (int) this.training.getRecordCount();


    final EngineData pair = BasicEngineData.createPair(
        flat.getInputCount(), flat.getOutputCount());


    this.inputArray = new float[training.getInputSize() * this.trainingLength];
    this.idealArray = new float[training.getIdealSize() * this.trainingLength];
    
    int inputIndex = 0;
    int idealIndex = 0;


    for (int i = 0; i < this.trainingLength; i++) {
      training.getRecord(i, pair);
      for (int col = 0; col < flat.getInputCount(); col++) {
        this.inputArray[inputIndex++] = (float) pair.getInputArray()[col];
      }


      for (int col = 0; col < flat.getOutputCount(); col++) {
        this.idealArray[idealIndex++] = (float) pair.getIdealArray()[col];
      }
    }


    final int errorSize = (int) training.getRecordCount();
    this.errors = new float[errorSize];

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    this.inputArray = new float[inputSize * this.trainingLength];
    this.idealArray = new float[idealSize * this.trainingLength];
    this.paramArray = new int[10];


    final EngineData pair = BasicEngineData.createPair(
        flat.getInputCount(), flat.getOutputCount());


    int inputIndex = 0;
    int idealIndex = 0;


    for (int i = 0; i < this.trainingLength; i++) {
      training.getRecord(i, pair);
      for (int col = 0; col < flat.getInputCount(); col++) {
        this.inputArray[inputIndex++] = (float) pair.getInputArray()[col];
      }


      for (int col = 0; col < flat.getOutputCount(); col++) {
        this.idealArray[idealIndex++] = (float) pair.getIdealArray()[col];
      }
    }


  }

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Related Classes of org.encog.engine.data.EngineData

org.encog.engine.network.flat.FlatNetwork

org.encog.engine.opencl.kernels.KernelNetworkCalc

org.encog.engine.opencl.kernels.KernelNetworkTrain

org.neuroph.core.learning.TrainingSet

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