Examples of ArchitectureLayer

Examples of org.encog.ml.factory.parse.ArchitectureLayer

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      throw new EncogError(
          "SRN Networks must have exactly three elements, "
          + "separated by ->.");
    }

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

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

    RBFEnum t;

    if (rbfLayer.getName().equalsIgnoreCase("Gaussian")) {
      t = RBFEnum.Gaussian;
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Examples of org.encog.ml.factory.parse.ArchitectureLayer

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    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.");
    }
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Examples of org.encog.ml.factory.parse.ArchitectureLayer

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    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);
    final ArchitectureLayer outputLayer = ArchitectureParse.parseLayer(
        layers.get(1), output);

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

    final SOMPattern pattern = new SOMPattern();
    pattern.setInputNeurons(inputCount);
    pattern.setOutputNeurons(outputCount);
    return pattern.generate();
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Examples of org.encog.ml.factory.parse.ArchitectureLayer

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      throw new EncogError(
          "RBF Networks must have exactly three elements, "
          + "separated by ->.");
    }

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

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

    RBFEnum t;

    if (rbfLayer.getName().equalsIgnoreCase("Gaussian")) {
      t = RBFEnum.Gaussian;
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Examples of org.encog.ml.factory.parse.ArchitectureLayer

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        defaultCount = input;
      } else {
        defaultCount = output;
      }

      final ArchitectureLayer layer = ArchitectureParse.parseLayer(
          layerStr, defaultCount);
      final boolean bias = layer.isBias();

      String part = layer.getName();
      if (part != null) {
        part = part.trim();
      } else {
        part = "";
      }
     
      ActivationFunction lookup = this.factory.create(part);
     
      if (lookup!=null) {
        af = lookup;
      } else {
        if (layer.isUsedDefault()) {
          questionPhase++;
          if (questionPhase > 2) {
            throw new EncogError("Only two ?'s may be used.");
          }
        }

        if (layer.getCount() == 0) {
          throw new EncogError("Unknown architecture element: "
              + architecture + ", can't parse: " + part);
        }

        result.addLayer(new BasicLayer(af, bias,
            layer.getCount()));

      }
    }

    result.getStructure().finalizeStructure();
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Examples of org.encog.ml.factory.parse.ArchitectureLayer

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      throw new EncogError(
          "PNN Networks must have exactly three elements, "
          + "separated by ->.");
    }

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

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

    PNNKernelType kernel;
    PNNOutputMode outmodel;

    if (pnnLayer.getName().equalsIgnoreCase("c")) {
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Examples of org.encog.ml.factory.parse.ArchitectureLayer

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      throw new EncogError(
          "PNN Networks must have exactly three elements, "
          + "separated by ->.");
    }

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

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

    PNNKernelType kernel;
    PNNOutputMode outmodel;

    if (pnnLayer.getName().equalsIgnoreCase("c")) {
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Examples of org.encog.ml.factory.parse.ArchitectureLayer

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      throw new EncogError(
          "SRN Networks must have exactly three elements, "
          + "separated by ->.");
    }

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

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

    RBFEnum t;

    if (rbfLayer.getName().equalsIgnoreCase("Gaussian")) {
      t = RBFEnum.Gaussian;
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Examples of org.encog.ml.factory.parse.ArchitectureLayer

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    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.");
    }
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Examples of org.encog.ml.factory.parse.ArchitectureLayer

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    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);
    final ArchitectureLayer outputLayer = ArchitectureParse.parseLayer(
        layers.get(1), output);

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

    final SOMPattern pattern = new SOMPattern();
    pattern.setInputNeurons(inputCount);
    pattern.setOutputNeurons(outputCount);
    return pattern.generate();
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