Package org.encog

Examples of org.encog.EncogError

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    try {
      this.program = context.createProgram(calcLayerFloat).build();
      this.kernel = program.createKernel("calculateLayer");
    } catch (CLBuildException e) {
      throw new EncogError(e);
    }
  }
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  public final MLTrain create(final MLMethod method,
      final MLDataSet training,
      final String args) {

    if (!(method instanceof BasicPNN)) {
      throw new EncogError(
          "PNN training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    return new TrainBasicPNN((BasicPNN) method, training);
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    /**
     * {@inheritDoc}
     */
    @Override
    public final void remove() {
      throw new EncogError("Called remove, unsupported operation.");
    }
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  public final  MLTrain create(final MLMethod method,
      final MLDataSet training,
      final String argsStr) {

    if (!(method instanceof SOM)) {
      throw new EncogError(
          "Cluster SOM training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    return new SOMClusterCopyTraining((SOM) method, training);
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  public final MLTrain create(final MLMethod method,
      final MLDataSet training,
      final String args) {

    if (!(method instanceof BasicNetwork)) {
      throw new EncogError(
          "SCG training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    return new ScaledConjugateGradient((BasicNetwork) method, training);
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  public final MLTrain create(final MLMethod method,
      final MLDataSet training,
      final String args) {

    if (!(method instanceof RBFNetwork)) {
      throw new EncogError(
          "RBF-SVD training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    return new SVDTraining((RBFNetwork) method, training);
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   * @param file
   *            The file to write to.
   */
  public final void normalize(final File file) {
    if (this.analyst == null) {
      throw new EncogError(
          "Can't normalize yet, file has not been analyzed.");
    }

    ReadCSV csv = null;
    PrintWriter tw = null;
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  public final MLMethod create(final String architecture, final int input,
      final int output) {

    final List<String> layers = ArchitectureParse.parseLayers(architecture);
    if (layers.size() != MAX_LAYERS) {
      throw new EncogError(
          "SVM's must have exactly three elements, separated by ->.");
    }

    final ArchitectureLayer inputLayer = ArchitectureParse.parseLayer(
        layers.get(0), input);
    final ArchitectureLayer paramsLayer = ArchitectureParse.parseLayer(
        layers.get(1), input);
    final ArchitectureLayer outputLayer = ArchitectureParse.parseLayer(
        layers.get(2), output);

    final String name = paramsLayer.getName();
    final String kernelStr = paramsLayer.getParams().get("KERNEL");
    final String svmTypeStr = paramsLayer.getParams().get("TYPE");

    SVMType svmType = SVMType.NewSupportVectorClassification;
    KernelType kernelType = KernelType.RadialBasisFunction;

    boolean useNew = true;

    if (svmTypeStr == null) {
      useNew = true;
    } else if (svmTypeStr.equalsIgnoreCase("NEW")) {
      useNew = true;
    } else if (svmTypeStr.equalsIgnoreCase("OLD")) {
      useNew = false;
    } else {
      throw new EncogError("Unsupported type: " + svmTypeStr
          + ", must be NEW or OLD.");
    }

    if (name.equalsIgnoreCase("C")) {
      if (useNew) {
        svmType = SVMType.NewSupportVectorClassification;
      } else {
        svmType = SVMType.SupportVectorClassification;
      }
    } else if (name.equalsIgnoreCase("R")) {
      if (useNew) {
        svmType = SVMType.NewSupportVectorRegression;
      } else {
        svmType = SVMType.EpsilonSupportVectorRegression;
      }
    } else {
      throw new EncogError("Unsupported mode: " + name
          + ", must be C for classify or R for regression.");
    }

    if (kernelStr == null) {
      kernelType = KernelType.RadialBasisFunction;
    } else if ("linear".equalsIgnoreCase(kernelStr)) {
      kernelType = KernelType.Linear;
    } else if ("poly".equalsIgnoreCase(kernelStr)) {
      kernelType = KernelType.Poly;
    } else if ("precomputed".equalsIgnoreCase(kernelStr)) {
      kernelType = KernelType.Precomputed;
    } else if ("rbf".equalsIgnoreCase(kernelStr)) {
      kernelType = KernelType.RadialBasisFunction;
    } else if ("sigmoid".equalsIgnoreCase(kernelStr)) {
      kernelType = KernelType.Sigmoid;
    } else {
      throw new EncogError("Unsupported kernel: " + kernelStr
          + ", must be linear,poly,precomputed,rbf or sigmoid.");
    }

    final int inputCount = inputLayer.getCount();
    final int outputCount = outputLayer.getCount();

    if (outputCount != 1) {
      throw new EncogError("SVM can only have an output size of 1.");
    }

    final SVM result = new SVM(inputCount, svmType, kernelType);

    return result;
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  public final MLMethod create(final String architecture, final int input,
      final int output) {

    final List<String> layers = ArchitectureParse.parseLayers(architecture);
    if (layers.size() != 2) {
      throw new EncogError(
          "SOM's must have exactly two elements, separated by ->.");
    }

    final ArchitectureLayer inputLayer = ArchitectureParse.parseLayer(
        layers.get(0), input);
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   */
  public final MLTrain create(final MLMethod method,
      final MLDataSet training, final String argsStr) {

    if (!(method instanceof ContainsFlat)) {
      throw new EncogError(
          "RPROP training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
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Related Classes of org.encog.EncogError

  • org.encog.app.analyst.csv.normalize.AnalystNormalizeCSV
  • org.encog.app.analyst.script.prop.PropertyConstraints
  • org.encog.engine.network.activation.ActivationCompetitive
  • org.encog.examples.neural.image.ImageNeuralNetwork
  • org.encog.mathutil.Equilateral
  • org.encog.mathutil.randomize.FanInRandomizer
  • org.encog.mathutil.randomize.NguyenWidrowRandomizer
  • org.encog.ml.data.auto.AutoFloatDataSet
  • org.encog.ml.data.auto.AutoFloatDataSet$AutoFloatIterator
  • org.encog.ml.data.basic.BasicMLDataSet$BasicMLIterator
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