/**
* Create a NEAT population.
*
* @param architecture The architecture string to use.
* @param input The input count.
* @param output The output count.
* @return The population.
*/
public MLMethod create(final String architecture, final int input, final int output) {
if (input <= 0) {
throw new EncogError("Must have at least one input for NEAT.");
}
if (output <= 0) {
throw new EncogError("Must have at least one output for NEAT.");
}
final Map<String, String> args = ArchitectureParse.parseParams(architecture);
final ParamsHolder holder = new ParamsHolder(args);
final int populationSize = holder.getInt(MLMethodFactory.PROPERTY_POPULATION_SIZE, false, 1000);
final int cycles =
holder.getInt(MLMethodFactory.PROPERTY_CYCLES, false, NEATPopulation.DEFAULT_CYCLES);
ActivationFunction af =
this.factory.create(
holder.getString(MLMethodFactory.PROPERTY_AF, false, MLActivationFactory.AF_SSIGMOID));
NEATPopulation pop = new NEATPopulation(input, output, populationSize);
pop.reset();
pop.setActivationCycles(cycles);
pop.setNEATActivationFunction(af);
return pop;
}
/**
* Create a LMA trainer.
*
* @param method The method to use.
* @param training The training data to use.
* @param argsStr The arguments to use.
* @return The newly created trainer.
*/
public MLTrain create(final MLMethod method, final MLDataSet training, final String argsStr) {
if (!(method instanceof SOM)) {
throw new EncogError(
"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);
}
return result;
}
/**
* Create a Manhattan trainer.
*
* @param method The method to use.
* @param training The training data to use.
* @param argsStr The arguments to use.
* @return The newly created trainer.
*/
public final 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);
}
/**
* Create a SVM trainer.
*
* @param method The method to use.
* @param training The training data to use.
* @param argsStr The arguments to use.
* @return The newly created trainer.
*/
public MLTrain create(final MLMethod method, final MLDataSet training, final String argsStr) {
if (!(method instanceof SVM)) {
throw new EncogError(
"SVM Train training cannot be used on a method of type: " + method.getClass().getName());
}
final double defaultGamma = 1.0 / ((SVM) method).getInputCount();
final double defaultC = 1.0;
final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
final ParamsHolder holder = new ParamsHolder(args);
final double gamma = holder.getDouble(MLTrainFactory.PROPERTY_GAMMA, false, defaultGamma);
final double c = holder.getDouble(MLTrainFactory.PROPERTY_C, false, defaultC);
final SVMTrain result = new SVMTrain((SVM) method, training);
result.setGamma(gamma);
result.setC(c);
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 a SVM trainer.
*
* @param method The method to use.
* @param training The training data to use.
* @param argsStr The arguments to use.
* @return The newly created trainer.
*/
public MLTrain create(final MLMethod method, final MLDataSet training, final String argsStr) {
if (!(method instanceof SVM)) {
throw new EncogError(
"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);
result.setGammaStep(gammaStep);
result.setConstBegin(cStart);
result.setConstEnd(cStop);
result.setConstStep(cStep);
return result;
}
