/**
* Create a feed forward network.
*
* @param architecture The architecture string to use.
* @param input The input count.
* @param output The output count.
* @return The feedforward network.
*/
public final MLMethod create(final String architecture, final int input, final int output) {
if (input <= 0) {
throw new EncogError("Must have at least one input for feedforward.");
}
if (output <= 0) {
throw new EncogError("Must have at least one output for feedforward.");
}
final BasicNetwork result = new BasicNetwork();
final List<String> layers = ArchitectureParse.parseLayers(architecture);
ActivationFunction af = new ActivationLinear();
int questionPhase = 0;
for (final String layerStr : layers) {
int defaultCount;
// determine default
if (questionPhase == 0) {
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();
result.reset();
return result;
}
/**
* Create a RBF network.
*
* @param architecture THe architecture string to use.
* @param input The input count.
* @param output The output count.
* @return The RBF network.
*/
public 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("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;
} else if (rbfLayer.getName().equalsIgnoreCase("Multiquadric")) {
t = RBFEnum.Multiquadric;
} else if (rbfLayer.getName().equalsIgnoreCase("InverseMultiquadric")) {
t = RBFEnum.InverseMultiquadric;
} else if (rbfLayer.getName().equalsIgnoreCase("MexicanHat")) {
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;
}
/**
* Create the SVM.
*
* @param architecture The architecture string.
* @param input The input count.
* @param output The output count.
* @return The newly created SVM.
*/
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;
}
