Package org.encog.util

Examples of org.encog.util.ParamsHolder

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   */
  public MLTrain create(final MLMethod method,
      final MLDataSet training, final String argsStr) {

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);

    final double learningRate = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.7);
    final double momentum = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_MOMENTUM, false, 0.3);

    return new Backpropagation((BasicNetwork) method, training,
        learningRate, momentum);
  }
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      t = RBFEnum.MexicanHat;
    } else {
      throw new NeuralNetworkError("Unknown RBF: " + rbfLayer.getName());
    }

    final ParamsHolder holder = new ParamsHolder(rbfLayer.getParams());

    final int rbfCount = holder.getInt("C", true, 0);

    final RBFNetwork result = new RBFNetwork(inputCount, rbfCount,
        outputCount, t);

    return result;
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          "Neighborhood training cannot be used on a method of type: "
              + method.getClass().getName());
    }

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);

    final double learningRate = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.7);
    final String neighborhoodStr = holder.getString(
        MLTrainFactory.PROPERTY_NEIGHBORHOOD, false, "rbf");
    final String rbfTypeStr = holder.getString(
        MLTrainFactory.PROPERTY_RBF_TYPE, false, "gaussian");

    RBFEnum t;

    if (rbfTypeStr.equalsIgnoreCase("Gaussian")) {
      t = RBFEnum.Gaussian;
    } else if (rbfTypeStr.equalsIgnoreCase("Multiquadric")) {
      t = RBFEnum.Multiquadric;
    } else if (rbfTypeStr.equalsIgnoreCase("InverseMultiquadric")) {
      t = RBFEnum.InverseMultiquadric;
    } else if (rbfTypeStr.equalsIgnoreCase("MexicanHat")) {
      t = RBFEnum.MexicanHat;
    } else {
      t = RBFEnum.Gaussian;
    }

    NeighborhoodFunction nf = null;

    if (neighborhoodStr.equalsIgnoreCase("bubble")) {
      nf = new NeighborhoodBubble(1);
    } else if (neighborhoodStr.equalsIgnoreCase("rbf")) {
      final String str = holder.getString(
          MLTrainFactory.PROPERTY_DIMENSIONS, true, null);
      final int[] size = NumberList.fromListInt(CSVFormat.EG_FORMAT, str);
      nf = new NeighborhoodRBF(size, t);
    } else if (neighborhoodStr.equalsIgnoreCase("rbf1d")) {
      nf = new NeighborhoodRBF1D(t);
    }
    if (neighborhoodStr.equalsIgnoreCase("single")) {
      nf = new NeighborhoodSingle();
    }

    final BasicTrainSOM result = new BasicTrainSOM((SOM) method,
        learningRate, training, nf);

    if (args.containsKey(MLTrainFactory.PROPERTY_ITERATIONS)) {
      final int plannedIterations = holder.getInt(
          MLTrainFactory.PROPERTY_ITERATIONS, false, 1000);
      final double startRate = holder.getDouble(
          MLTrainFactory.PROPERTY_START_LEARNING_RATE, false, 0.05);
      final double endRate = holder.getDouble(
          MLTrainFactory.PROPERTY_END_LEARNING_RATE, false, 0.05);
      final double startRadius = holder.getDouble(
          MLTrainFactory.PROPERTY_START_RADIUS, false, 10);
      final double endRadius = holder.getDouble(
          MLTrainFactory.PROPERTY_END_RADIUS, false, 1);
      result.setAutoDecay(plannedIterations, startRate, endRate,
          startRadius, endRadius);
    }
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   * @return The newly created trainer.
   */
  public MLTrain create(final MLMethod method,
      final MLDataSet training, final String argsStr) {
    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);

    final int maxParents = holder.getInt(
        MLTrainFactory.PROPERTY_MAX_PARENTS, false, 1);
    String searchStr = holder.getString("SEARCH", false, "k2");
    String estimatorStr = holder.getString("ESTIMATOR", false, "simple");
    String initStr = holder.getString("INIT", false, "naive");
   
    BayesSearch search;
    BayesEstimator estimator;
    BayesianInit init;
   
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          "SVM Train training cannot be used on a method of type: "
              + method.getClass().getName());
    }
   
    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    new ParamsHolder(args);

    final ParamsHolder holder = new ParamsHolder(args);
    final double gammaStart = holder.getDouble(SVMSearchFactory.PROPERTY_GAMMA1, false, SVMSearchTrain.DEFAULT_GAMMA_BEGIN);
    final double cStart = holder.getDouble(SVMSearchFactory.PROPERTY_C1, false, SVMSearchTrain.DEFAULT_CONST_BEGIN);
    final double gammaStop = holder.getDouble(SVMSearchFactory.PROPERTY_GAMMA2, false, SVMSearchTrain.DEFAULT_GAMMA_END);
    final double cStop = holder.getDouble(SVMSearchFactory.PROPERTY_C2, false, SVMSearchTrain.DEFAULT_CONST_END);
    final double gammaStep = holder.getDouble(SVMSearchFactory.PROPERTY_GAMMA_STEP, false, SVMSearchTrain.DEFAULT_GAMMA_STEP);
    final double cStep = holder.getDouble(SVMSearchFactory.PROPERTY_C_STEP, false, SVMSearchTrain.DEFAULT_CONST_STEP);
   
    final SVMSearchTrain result = new SVMSearchTrain((SVM)method, training);
   
    result.setGammaBegin(gammaStart);
    result.setGammaEnd(gammaStop);
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   */
  public MLTrain create(final MLMethod method,
      final MLDataSet training, final String argsStr) {

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);

    final double learningRate = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.1);

    return new ManhattanPropagation((BasicNetwork) method, training,
        learningRate);
  }
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   */
  public MLTrain create(final MLMethod method,
      final MLDataSet training, final String argsStr) {

    final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
    final ParamsHolder holder = new ParamsHolder(args);

    final double learningRate = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_RATE, false, 2.0);
   
    return new QuickPropagation((BasicNetwork) method, training, learningRate);
  }
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Related Classes of org.encog.util.ParamsHolder

  • org.encog.EncogError
  • org.encog.ml.factory.method.EPLFactory
  • org.encog.ml.factory.method.NEATFactory
  • org.encog.ml.factory.method.PNNFactory
  • org.encog.ml.factory.method.RBFNetworkFactory
  • org.encog.ml.factory.train.AnnealFactory
  • org.encog.ml.factory.train.BackPropFactory
  • org.encog.ml.factory.train.GeneticFactory
  • org.encog.ml.factory.train.LMAFactory
  • org.encog.ml.factory.train.ManhattanFactory
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